Next Article in Journal
The Psychometric Properties and Effectiveness of the Approach-Avoidance Job Crafting Scale among Chinese Kindergarten Teachers
Next Article in Special Issue
Burnout and Quality of Work Life among Physicians during Internships in Public Hospitals in Thailand
Previous Article in Journal
Coping with the Stress through Individual and Contextual Resilient Factors in Primary School Settings
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Behavioral Sciences
Volume 13
Issue 11
10.3390/bs13110881
behavsci-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Social Network Characteristics and Depressive Symptoms of Italian Public Health Medical Residents: The Public Health Residents’ Anonymous Survey in Italy (PHRASI)

by
Lorenzo Stacchini
1,
Alessandro Catalini
2,
Valentina De Nicolò
3,*,
Claudia Cosma
1,
Veronica Gallinoro
1,
Angela Ancona
4,
Nausicaa Berselli
5,
Marta Caminiti
6,
Clara Mazza
7,
Giuseppa Minutolo
8,
Fabrizio Cedrone
9,
Vincenza Gianfredi
10 and
on Behalf of the Working Group on Public Mental Health 2021/2022 of the Medical Residents’ Assembly of the Italian Society of Hygiene and Preventive Medicine
†
1
Department of Health Sciences, University of Florence, 50121 Florence, Italy
2
Department of Biomedical Sciences and Public Health, Università Politecnica delle Marche, 60100 Ancona, Italy
3
Department of Public Health and Infectious Disease, Sapienza University of Rome, 00185 Rome, Italy
4
School of Hygiene and Preventive Medicine, Vita-Salute San Raffaele University, 20132 Milan, Italy
5
Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, 41125 Modena, Italy
6
Department of Medicine and Surgery—Sector of Public Health, University of Perugia, 06100 Perugia, Italy
7
Department of Public Health, Experimental and Forensic Medicine, University of Pavia, 27100 Pavia, Italy
8
Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, University of Palermo, 90127 Palermo, Italy
9
Hospital Management, Local Health Authority of Pescara, 65100 Pescara, Italy
10
Department of Biomedical Sciences for Health, University of Milan, 20133 Milan, Italy
*
Author to whom correspondence should be addressed.
†
Membership of the Working Group is Provided in the Acknowledgements.
Behav. Sci. 2023, 13(11), 881; https://doi.org/10.3390/bs13110881
Submission received: 1 August 2023 / Revised: 26 September 2023 / Accepted: 16 October 2023 / Published: 25 October 2023
(This article belongs to the Special Issue Stress, Anxiety, and Depression among Healthcare Workers)
Browse Figures
Versions Notes

Abstract

:
Despite the high impact of the COVID-19 pandemic on social interactions and healthcare workers’ (HWs’) mental health, few studies have investigated the association between social network characteristics and HWs’ mental health, particularly during the pandemic. Therefore, we aimed to assess the associations between public health residents’ (PHRs’) social network characteristics and depression. We used data from the Public Health Residents’ Anonymous Survey in Italy (PHRASI), a nationwide cross-sectional study. Social network characteristics were self-reported. Depressive symptoms were assessed using the nine-item Patient Health Questionnaire. Linear and logistic models adjusted for age, sex, physical activity, and alcohol were used. A moderation analysis by sex was also performed. A total of 379 PHRs participated in the survey (58% females, median age of 30 years). More peer-to-peer (odds ratio OR = 0.62 (0.47–0.83)) and supervisor support (OR = 0.49 (0.36–0.68)), more social participation ((OR) = 0.36 95% CI (0.25–0.50)), and having a partner (OR = 0.49 (0.25–0.96)) were significantly associated with a lower risk of clinically relevant depressive symptoms. Work-to-private-life interference was significantly associated with a higher risk of clinically relevant depressive symptoms (OR = 1.77 (1.28–2.45)). Promoting a supportive work environment and social participation as well as reducing work-to-private life interference can contribute to reducing the high burden among PHRs.

Graphical Abstract

1. Introduction

Depression is one of the most prevalent mental illnesses, affecting 3.8% of the global population and resulting in approximately 280 million people suffering from depression [1,2]. Depression is a remittent disease that represents a main determinant of years lived with disability [3], an excess of mortality, and a reduction in life expectancy (14 years in males and 10 years in females) [4,5]. Because of the high burden of the disease and the associated healthcare costs, the World Health Organization (WHO) defines depression as a major public health problem [6]. Moreover, the WHO’s projections estimate that depression will increase, becoming the leading cause of burden in 2030 [1,7].
Despite efforts, the causes of depression are not completely known, but there is recognition of a multifactorial etiology, including genetic, environmental, biological, psychosocial, and social components [8]. Among them, the rapid spread of the COVID-19 pandemic greatly impacted mental health, leading to a stark rise in depressive disorders, with an estimated increase of 27.6% [range 25.1–30.3%] and an additional 53.2 million [95% uncertainty interval 44.8–62.9] cases of depression worldwide [9]. Generally, restrictive measures put in place to stop the spread of COVID-19 infection played an important role in dramatically increasing the overall number of depression cases [10]. In more detail, quarantine and social distancing further increased the risk of social isolation, loneliness, and depression [11,12], highlighting how social isolation is strictly linked to physical [13] and mental health [14,15,16,17,18]. Regarding mental health, various studies [19,20,21,22] have shown that social networks, i.e., systems of social relationships in which individuals embed [23,24], are related to depressive disorder. They particularly pointed out the association between limited support networks and lower psychological well-being [18,25,26] and the prominent role of a supportive network in providing psychological advantages and reducing mental distress [27]. Furthermore, evidence highlights that low social integration, i.e., individual participation in social relationships and engagement in social activities [28], is associated with poor mental health outcomes [29].
On the contrary, the COVID-19 pandemic led to unprecedented restrictions on social life and drastically impacted social integration and its protective role in mental well-being [30]. According to a recent meta-analysis (2021), the social impact of the COVID-19 pandemic imposed a huge mental health burden on society, especially on quarantined people, COVID-19 patients, and healthcare workers compared with the general population [31]. Despite the high impact of the pandemic on healthcare workers’ mental health, few studies have been conducted to investigate how social networks and social support could influence healthcare workers’ mental health, particularly during the COVID-19 pandemic. Moreover, previous studies have mainly focused on structured healthcare workers, and no research has been conducted during the years of residency, particularly among public health residents (PHRs).
In order to bridge this gap in knowledge, the current nationwide, cross-sectional study aimed to assess the associations between PHRs’ social network characteristics and depression. We hypothesized that a low and not supportive social network might be associated with an increased risk of depression during residency. Lastly, we performed a moderation analysis by sex to examine if the effect of social network characteristics on depressive symptoms is the same across genders.

2. Materials and Methods

2.1. Study Population and Design

This study originated from the Public Health Residents’ Anonymous Survey in Italy (PHRASI), a nationwide, cross-sectional investigation aimed at assessing the mental health of Italian Public Health Medical Residents and its determinants.
PHRASI comprises an 88-item online questionnaire, which includes socio-demographic questions and tools for evaluating various aspects of mental health. All the questionnaires used in the survey were drawn from the existing literature and had been previously validated. To ensure internal consistency, we calculated the Cronbach’s α values for each of the adopted tools (Supplementary Table S1). The study protocol was published previously [32]. In brief, the administration of the survey using Google Forms was made possible through the robust network of the Medical Residents’ Assembly of the Italian Society of Hygiene and Preventive Medicine. The survey link was distributed to all Italian postgraduate Public Health school residents via a mailing list. Furthermore, to enhance participation, researchers directly contacted each representative to encourage the distribution of the survey among their colleagues. Participation was voluntary and anonymous, and no incentives were provided for participating. Data collection began on 14 June 2022 and finished on 26 July 2022. A sample size of 314 was calculated using the Charan and Biswas formula [33], taking into account the expected lifetime prevalence of mental health disorders (28.5%) [34] and the expected error of the confidence interval estimate (5.0%).

2.2. Social Network Characteristics

Both functional and structural characteristics of the social network were assessed. Regarding functional characteristics of the social network, peer-to-peer support, supervisor support, and work-to-private life interference were measured, using Likert scale questions as follows: “I can rely on the help of my colleagues” (1—Never; 5—Always), “I can rely on the help of my boss” (1—Never; 5—Always), “My work often interferes with my family, social or personal duties” (1—Always; 5—Never) [35].
For the structural characteristics of the social network, the distance between the residential and working regions, the number of family members, cohabitation, having a partner, and social participation were assessed. The distance between the residential and working regions was determined by measuring the distance between the geographical center (centroid) of each region and the centroid of the other region using the Vincenty method [36]. The number of family members was calculated by taking into account cohabitation, having a partner, and the number of children. Cohabitation refers to living with someone (e.g., a flatmate or partner) in the same household. Having a partner was assessed by inquiring whether the participant was in a stable relationship (yes/no). Social participation was assessed using the 5th item of the WHO-5 Well-being questionnaire [37], which asks, “My daily life has been filled with things that interest me”. The WHO-5 Well-being is a validated questionnaire based on a 5-item Likert scale (0—at no time; 5—all of the time = 5) [37].

2.3. Assessment of Depressive Symptoms

Prevalent depressive symptoms were assessed using a validated Italian version of the 9-item Patient Health Questionnaire (PHQ-9) [38]. This questionnaire consists of nine items rated on a four-point scale, ranging from 0 = “not at all” to 3 = “nearly every day”. The response options were utilized to compute a continuous total score, which ranges from 0 (indicating the absence of symptoms) to 27 (indicating the presence of all symptoms nearly every day). For the purpose of this study, two predefined cut-offs were applied to identify (i) the presence of mild to severe depressive symptoms, with a cut-off score equal to or greater than 5 [39], and (ii) clinically relevant depressive symptoms (moderate to severe), with a cut-off score equal to or greater than 10.

2.4. Covariates

The administered questionnaire allowed us to gather sociodemographic data, including gender and age. Additionally, lifestyle factors such as physical activity and alcohol consumption were also assessed. Alcohol consumption was evaluated using the Alcohol Use Disorders Identification Test (AUDIT-C) [40,41]. AUDIT-C comprises 3 questions, with each question having a scale ranging from 0 to 4, resulting in a total score ranging from 0 to 12. The total score was subsequently dichotomized into “high-risk drinking” (equal to or greater than 5 for males, and equal to or greater than 4 for females). Physical activity was measured using the International Physical Activity Questionnaire (IPAQ) [42] in Italian, provided by the Italian Society of Endocrinology (ISE). The metabolic equivalent task was calculated following ISE indication. Subsequently, we categorized each participant into three groups based on IPAQ score: a total score of <700 was classified as “Insufficiently active”, a score between 700 and 2519 was classified as “Sufficiently active”, and a score exceeding 2519 was classified as “Active”.

2.5. Statistical Analysis

Continuous variables were provided as median and interquartile range, while categorical variables were described as frequencies and percentages. To examine the relationships between predictor variables, a correlation matrix was generated using Kendall’s tau correlation coefficient. Multivariate linear regression models, adjusted for sex and age, were performed with the PHQ-9 score as the continuous dependent variable (model 1). Additionally, multivariate logistic regressions were performed using the PHQ-9 score as a dichotomous dependent variable to assess the presence of depressive symptoms, yielding odd ratios (ORs) and 95% confidence intervals (95% CIs) (model 2). In this case, two pre-defined PHQ-9 cut-offs were considered. Furthermore, a sensitivity analysis was carried out in which the multivariate regression models were additionally adjusted for alcohol consumption (assessed using the AUDIT-C risk category) and physical activity (assessed using IPAQ activity category) (model 3). A moderation analysis was conducted by sex, with females as a reference, using multivariate logistic regressions (outcome: PHQ-9 ≥ 10) adjusted by age for each independent variable. An interaction term was created, and significant effects were identified when the predictor and the moderator significantly affected the outcome. In cases where the interaction term was not significant, the predictor OR applies to both males and females. However, when significant, the interaction term applies to males, and the predictor to females. Associations with p < 0.05 were considered statistically significant when using two-sided tests.
To assess multicollinearity, we examined Kendall’s tau correlation coefficient [43] between each independent variable, considering a strong correlation when Kendall’s tau correlation coefficient (τ) was equal to or greater than 0.50. All the analyses were performed using R 4.2.3.

2.6. Ethical Approval

This study did not require the approval of an ethics committee because the questionnaire data were anonymous, rendering it impossible to identify any respondent [44,45,46]. All data were entered into an anonymous password-protected computer database.

3. Results

3.1. Descriptive Characteristics of the Sample

The characteristics of the sample are summarized in Table 1. A total of 379 subjects participated to the Public Health Residents’ Anonymous Survey in Italy (PHRASI) study. The majority of young physicians who completed the questionnaire were females (58%). The median age of the sample was 30 years, with an interquartile range between 29 and 34. Most of the subjects had a partner (73%) and lived with others (74%). Mean, SD, skewness, and kurtosis are reported in Supplementary Table S2, and the statistical distribution is shown in Supplementary Figure S1. The correlation matrix between the study variables is reported in Supplementary Table S3.
Mild to severe depressive symptoms (PHQ-9 ≥ 5) were reported by 231 participants (59% females, 31 years old), whereas clinically relevant depressive symptoms (PHQ-9 ≥ 10) were reported by 97 participants (57% females, 31 years old).

3.2. Social Network Characteristics and Depressive Symptoms

Table 2 shows the association between functional and structural characteristics of social networks and depressive symptoms, as determined using the linear regression models adjusted for sex and age. Among functional characteristics of social networks, greater peer-to-peer support and supervisor support were associated with a lower PHQ-9 score. Specifically, for each increment in peer-to-peer support (β = −1.01 95% CI = (−1.52; −0.50)) and supervisor support (β = −1.20 95% CI = (−1.66; −0.74)), depressive symptoms decreased. Conversely, as the value related to work-to-private-life interference increased (β = 1.24 95% CI = (0.74; 1.74)), the PHQ-9 score increased.
Regarding structural characteristics of social networks, family members, having a partner, and social participation were significantly associated with PHQ-9. Specifically, for each unit increase in social participation, depressive symptoms decreased on a continuous scale (β = −2.13 95% CI = (−2.49; −1.76)). Having more family members was significantly associated with lower depressive symptoms (β = −0.81 95% CI = (−1.54; 0.08)). Similarly, having a partner is associated with fewer depressive symptoms (β = −1.42 95% CI = (−2.58; −0.25)).

3.3. Social Network Characteristics and Clinically Relevant Depressive Symptoms

Table 3 shows the results of the logistic regression adjusted for sex and age, with the PHQ-9 ≥ 10 score considered as a dichotomous dependent variable.
Among the functional characteristics of the social network, both peer-to-peer and supervisor support are significantly associated with a lower risk of clinically relevant depressive symptoms (OR = 0.72 (95% CI = 0.58; 0.90), OR = 0.69 (95% CI = 0.55; 0.85), respectively). Conversely, work-to-private-life interference is statistically significantly associated with a higher risk of clinically relevant depressive symptoms (OR = 1.59 (95% CI = 1.25; 2.01)).
Among structural characteristics of the social network, having a partner and social participation are significantly associated with a lower risk of clinically relevant depressive symptoms (OR = 0.59 (95% CI = 0.36; 0.97), OR = 0.39 (95% CI = 0.31; 0.50), respectively). The distance between residential and working regions, cohabitation, and the number of family members are not statistically significantly associated with clinically relevant depressive symptoms. Similar results were also obtained when considering PHQ-9 ≥ 5 (Supplementary Table S4).
In the model further adjusted for alcohol consumption and physical activity (sensitivity analysis), social participation, peer-to-peer, supervisor support, having a partner, and WLI remain significantly associated (OR = 0.36 (95% CI = 0.25; 0.50); OR = 0.62 (95% CI = 0.47–0.83); OR = 0.49 (95% CI = [0.36–0.68); OR = 0.49 (95% CI = 0.25–0.96); OR = 1.77 (95% CI = 1.28–2.45), respectively), with clinically relevant depressive symptoms. Notably, social participation has a 3.12% reduction from the logistic regression adjusted for only age and sex, peer-to-peer support decreases by 16.13%, supervisor support decreases by 40.82%, and having a partner decreases by 20.41%, while WLI increases by 11.32% (Table 3).
Furthermore, we conducted a moderation analysis using sex as a moderator. Except for one moderation analysis, all others were found to be nonsignificant. Specifically, supervisor support is a protective factor for females (OR = 0.52; p < 0.001) while acting as a risk factor for depressive symptoms in males (OR = 1.77; p = 0.010). Detailed results are shown in Table 4.
The multicollinearity analysis revealed no collinearity (Kendall’s τ < 0.50) among the independent variables except for the pair “family members” and “cohabitation” (Figure 1).

4. Discussion

This study aims to assess the associations between the social network characteristics of Italian PHRs and depressive symptoms. In our analyses, with a primary focus on clinically relevant depressive symptoms (PHQ-9 ≥ 10), high social participation and having a partner emerge as protective factors, reducing the risk by 61% and 41%, respectively. Similarly, a supportive work environment with high peer-to-peer and supervisor support reduces the risk by 28% and 31%, respectively. On the other hand, high work-to-private life interference is associated with a 59% increased risk of clinically relevant depressive symptoms. The adoption of two cut-offs for PHQ-9, one for assessing mild-to-moderate depressive symptoms (PHQ-9 ≥ 5) and the other for clinically relevant depressive symptoms (PHQ-9 ≥ 10), allows for a more sensitive approach to identifying potential risks or protective factors. In our case, the results are comparable for both cut-offs, with the same variables showing statistically significant associations with the outcome, except that having a partner is significantly associated only with clinically relevant depressive symptoms. Similarly, when considering PHQ-9 as a continuous score, the results remain consistent. Moreover, to enhance this study’s internal consistency, a multicollinearity analysis was performed. The absence of a strong correlation between almost all the variables indicates that they measure different characteristics of the study population’s social network. The only notable correlation was found between family members and cohabitation (τ = 0.68), likely because family members are often part of the cohabiting group.
Our results are aligned with the scientific literature. Indeed, it has long been recognized that social support is strongly associated with mental health [47]. As noted by Holt-Lundstad et al. (2010), robust social relationships can significantly extend survival by 50%, while, conversely, poor social relationships may be more detrimental than excessive alcohol consumption, smoking, obesity, and lack of exercise [48]. Similarly, the role of social networks in mental health has been extensively studied, with reports consistently indicating that individuals with smaller networks, fewer interpersonal relationships, or lower levels of social support tend to have high rates of depression [49,50].
Among functional characteristics of the social network, both peer-to-peer and supervisor support were found to be protective against mild-to-severe and clinically relevant depressive symptoms, while work-to-private life interference emerged as a risk factor. A recent meta-analysis found that emotional forms of support are more closely associated with depression than instrumental forms of support, especially among adults aged 18–50 [51]. Regarding peer-to-peer support, the literature provides examples of its possible utility in reducing mental health symptoms. Interventions aimed at improving mental health in young people have demonstrated that peer-to-peer support effectively reduces negative effects [52]. Other studies have shown its positiveness on happiness, self-esteem effective coping, and reductions in depression, loneliness, and anxiety [53]. Furthermore, gender differences in this context are emphasized in the literature. Globally, females have a higher risk of suffering from depression and related symptoms than males [54,55], a situation that was exacerbated during the COVID-19 pandemic, possibly because females were more likely to be affected by its social consequences [56]. In light of these findings, we not only adjusted our regression models by sex but also conducted a moderation analysis to explore the relationship among each variable and clinically relevant depressive symptoms across different sexes. Supervisor support was found to be a risk factor for depressive symptoms in males, while it was protective for females. The positive association between supervisor support and healthcare workers’ mental well-being is supported by the literature [57,58]. Low supervisor support has also been shown to amply the association between social stressors and depressive symptoms [59]. However, when considering gender differences, supervisor support has been demonstrated to be a protective factor for work-related stress in females but not in males [60]. According to a recent cohort study, low supervisor support is a risk factor for depression in females but not in males [61]. These results can be interpreted in light of the masculine norms theory, which suggests that masculine norms reduce males’ willingness to seek and accept help during periods of psychological distress [62]. Therefore, it is possible that among our sample of male PHRs, a supportive supervisor could be perceived as a threat to their masculinity.
A high work-to-private life interference has previously been demonstrated as a predictive factor not only for depression but also for treatment in the general population [63]. In a longitudinal study, it was found that only females with high work-to-private-life interference were significantly more likely to develop major depression [64]. However, these specific results were not observed in our analyses. Despite the increasing focus on family friendly policies aimed at ensuring gender equality in Western countries, household tasks and responsibilities are still predominantly shouldered by females [65]. This suggests that a high work-to-private life interference may be more closely associated with females’ lives and their mental health than males. Nevertheless, it should be noted that our study population consisted of young adults who have not yet started a family, which could explain the absence of a gender difference in this association.
Among the structural characteristics of the social network in our study, having a partner was found to be a protective factor. This finding aligns with other studies investigating mental health in young adults, the age group to which most of the PHRs belong [66,67,68]. However, the literature also reports that depressive symptoms and romantic relationships are connected in a bidirectional manner: fewer positive partner interactions, such as intimacy and support, and more negative partner interactions, such as conflicts, are associated with increased depressive symptoms [69,70]. Conversely, depressive symptoms can impact romantic relationships by generating stress and potentially influencing interpersonal behavior and choices, which can, in turn, lead to the end of the relationship [71,72,73]. The cross-sectional design of our study does not allow us to delve deeper into whether these considerations are applicable to our study population. In this context, further investigations are needed to confirm these findings.
Similarly to romantic relationships, social participation has been observed to both influence and be influenced by good mental health status [74,75]. Leading a meaningful life with a sense of purpose has been identified as a determinant of health and is associated with a reduced risk of depression [76]. Our study’s findings are in line with the existing literature.

4.1. Implication and Practices for Public Health Policies

The medical residency period is a crucial phase in a physician’s professional journey. Medical residents are tasked with both practical responsibilities and deepening their theoretical knowledge, often without the immediate support of colleagues or superiors. In addition, the high workload and job responsibilities frequently encroach on their family, social, or personal life, which constitutes their social network. It is important to note that medical residents, as a group, are more likely to experience depressive symptoms than the general population, due to several factors, as reported in the literature [77]. This is especially true for Italian PHRs, who exhibit a high prevalence of depressive symptoms, similar to what is observed in healthcare workers in general [78,79].
Our findings are particularly relevant as they offer insight into the distribution of depressive symptoms among Italian PHRs, a population that has received limited attention in prior research. Moreover, while much of the existing research focuses on specific determinants of the social network, our study explores the association between several social network characteristics and depressive symptoms. This comprehensive assessment suggests the possible use of our data in terms of managing and organizing residency courses. In general, PHRs usually spend a significant portion of their residency in close contact with their supervisor and colleagues, but specific contextual factors also play a crucial role. For instance, in Italy, the high rate of retirements and a simultaneous low hiring rate in the healthcare system have led to a widespread shortage of personnel. The COVID-19 pandemic has underscored the urgent need to bolster the national healthcare system, resulting in an increase in scholarships for medical residents. However, this has led to fewer supervisors available for a larger number of PHRs enrolled in residency programs.
In light of this, the combination of external stressors, such as the COVID-19 pandemic, and the limited professional support available to PHRs may have contributed to the rising prevalence of depressive symptoms. Therefore, our findings can aid and inform policymakers in the management of residency training programs.
Furthermore, our results highlight the importance of applying a gender perspective in mental health studies to gain a proper understanding of the phenomenon and subsequently implement effective and proportionate interventions. Lastly, our study strengthens the need to explore gender differences in various mental health conditions among PHRs.

4.2. Limitations and Strengths

Before generalizing our results, the limitations and strengths of our work should be considered. Firstly, this is a cross-sectional study, which, by definition, makes it impossible to measure incidence or investigate the temporal relationship between exposures and outcomes. In a cross-sectional design, outcomes and exposures are measured simultaneously, and causality cannot be inferred. Particularly in this study, it is difficult to establish whether the absence of a supportive social network is a risk factor or a consequence of depressive symptoms. This is because depression often profoundly affects all aspects of life, and individuals with depression tend to have fewer rewarding and more dysfunctional social relationships compared with others [80]. Furthermore, participation in this study was voluntary, which could potentially introduce a selection bias. It is possible that individuals with depressive symptoms or those who are more socially isolated may be less prone to participate. Therefore, this type of potential selection bias may have resulted in an underestimation of the strength of the associations. Moreover, this study used self-reported measures, and people might have answered in accordance with social norms rather than truthfully [81]. Despite the precautions put in place, the results might be prone to social desirability bias [82].
Nevertheless, our study also has some strengths. It is a nationwide survey with a good representativeness of Italian PHRs [78]. In addition, it used a rigorous methodology that enhanced the internal consistency of the results. Furthermore, the role of gender was explored, considering the well-known influence of gender on mental health and healthcare accessibility [83,84,85]. Moreover, in terms of statistical analysis, we used advanced statistical techniques, considering depression as both a continuous and a dichotomous variable, and conducting linear and multiple logistic regression analyses. Moreover, we adjusted the model not only for sex and age but also for physical activity and alcohol. Lastly, to increase the robustness of our results, we conducted multicollinearity and moderation analyses. Across all these analyses, the main results remained consistent and did not materially change.

5. Conclusions

Our data contribute to building an evidence base of social network characteristics and their association with depressive symptoms among medical residents. Among the functional aspects of the social network, peer-to-peer and supervisor support are the most strongly associated with clinically relevant depressive symptoms, whereas, for the structural aspects, having a partner and social participation showed a protective role. Future research could delve into how the distribution of these social network characteristics persists and how they are associated with mental health, not only among medical residents but also among attending physicians and specialized medical doctors in general. In conclusion, our findings are valuable in understanding factors associated with depression and can contribute to reducing the high burden of mental health. These insights can inform policymakers and school directors in the planning and management of the training and professional activities of medical residents.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/bs13110881/s1, Table S1: Cronbach’s α values for each adopted questionnaire derived from the literature and estimated in the current study. Table S2: Descriptive statistics (mean, SD, skewness, kurtosis) of continuous variables; Figure S1: Distribution of continuous variables; Table S3: Correlation matrix of the predictor variables. Kendall’s tau correlation coefficient test was used; Table S4: Logistic regressions assessing the association between social network characteristics and mild-to-severe depressive symptoms.

Author Contributions

Conceptualization, V.G. (Vincenza Gianfredi), L.S., A.C. and V.D.N.; data curation, L.S. and V.G. (Vincenza Gianfredi); investigation, L.S., A.C.; V.D.N., C.C. and V.G. (Vincenza Gianfredi); methodology, V.G. (Vincenza Gianfredi), L.S., A.C. and V.D.N.; project administration, V.G. (Vincenza Gianfredi), L.S., A.C., V.D.N., A.A., N.B., M.C., C.M., G.M., F.C. and On Behalf of the Working Group on Public Mental Health 2021/2022 of the Medical Residents’ Assembly of the Italian Society of Hygiene and Preventive Medicine; resources, V.D.N. and C.C.; software, V.D.N. and C.C.; supervision, L.S., A.C., V.D.N., C.C., V.G. (Veronica Gallinoro), A.A., N.B., M.C., C.M., G.M., F.C. and V.G. (Vincenza Gianfredi); visualization, A.C. and V.G. (Vincenza Gianfredi); writing—original draft, V.D.N., A.A., C.C., V.G. (Veronica Gallinoro), L.S. and V.G. (Vincenza Gianfredi); writing—review and editing, V.D.N., L.S. and V.G. (Vincenza Gianfredi). All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Ethical review and approval was waived due to a perfectly anonymized and voluntary questionnaire.

Informed Consent Statement

Although the questionnaire is perfectly anonymous, it was possible to complete it only after declaring having understood the methods and purpose of this study and giving consent to the processing of personal data.

Data Availability Statement

The authors can be contacted for information about the data presented.

Acknowledgments

We are thankful to all the respondents. This study arises from a scientific collaboration within the Working Group on Public Mental Health of the medical residents’ Assembly of the Italian Society of Hygiene and Preventive Medicine. The authors of this article are all part of this group. We thank the other members of the group who have actively taken part in this project: Allegra Ferrari (University of Genova), Marianna Scarpaleggia (University of Genova), Giovanni Cicconi and Martina Cappellina (University of Milan), Gaia Ferraguzzi (University of Milan), Roberta Lattanzio (University of Milan), Enrica Cavarretta (University of Rome “Sapienza”), Elena Mazzoleni (University of Modena and Reggio Emilia), Céline Paudice (University of Pavia), Tiziana Ciarambino (University of Naples “Federico II”), Fabiano Grassi (University of Rome “Sapienza”) and Alessandro Berionni (University of Milan “San Raffaele”). We thank Lorenzo Blandi, National Coordinator, and all the other Board members of the medical residents’ Assembly of the Italian Society of Hygiene and Preventive Medicine, Virginia Casigliani, Marco Del Riccio, Melissa Corradi and Giacomo Pietro Vigezzi, for supporting the design of this survey. We thank Massimo Minerva (University of Milan “San Raffaele”) for sharing information about the number of post-graduate students enrolled in Public Health schools. Some of the graphical elements in the graphical abstract are 100% free images by pch.vector on Freepick.

Conflicts of Interest

The authors declare no conflict of interest regarding this manuscript.

References

  1. GBD 2019 Diseases and Injuries Collaborators. Global Burden of 369 Diseases and Injuries in 204 Countries and Territories, 1990–2019: A Systematic Analysis for the Global Burden of Disease Study 2019. Lancet 2020, 396, 1204–1222. [Google Scholar] [CrossRef] [PubMed]
  2. World Health Organization (WHO). Mental Disorders. Available online: https://www.who.int/news-room/fact-sheets/detail/mental-disorders (accessed on 30 March 2023).
  3. Vos, T.; Barber, R.M.; Bell, B.; Bertozzi-Villa, A.; Biryukov, S.; Bolliger, I.; Charlson, F.; Davis, A.; Degenhardt, L.; Dicker, D.; et al. Global, Regional, and National Incidence, Prevalence, and Years Lived with Disability for 301 Acute and Chronic Diseases and Injuries in 188 Countries, 1990–2013: A Systematic Analysis for the Global Burden of Disease Study 2013. Lancet 2015, 386, 743–800. [Google Scholar] [CrossRef] [PubMed]
  4. Cuijpers, P.; Vogelzangs, N.; Twisk, J.; Kleiboer, A.; Li, J.; Penninx, B.W. Comprehensive Meta-Analysis of Excess Mortality in Depression in the General Community Versus Patients With Specific Illnesses. Am. J. Psychiatry 2014, 171, 453–462. [Google Scholar] [CrossRef] [PubMed]
  5. Laursen, T.M.; Musliner, K.L.; Benros, M.E.; Vestergaard, M.; Munk-Olsen, T. Mortality and Life Expectancy in Persons with Severe Unipolar Depression. J. Affect. Disord. 2016, 193, 203–207. [Google Scholar] [CrossRef] [PubMed]
  6. World Health Organization (WHO). The Global Burden of Disease: 2004 Update. Available online: http://www.who.int/healthinfo/global_burden_disease/2004_report_update/en/ (accessed on 30 March 2023).
  7. GBD 2019 Mental Disorders Collaborators. Global, Regional, and National Burden of 12 Mental Disorders in 204 Countries and Territories, 1990–2019: A Systematic Analysis for the Global Burden of Disease Study 2019. Lancet Psychiatry 2022, 9, 137–150. [Google Scholar] [CrossRef] [PubMed]
  8. Engel, G.L. The Need for a New Medical Model: A Challenge for Biomedicine. Science 1977, 196, 129–136. [Google Scholar] [CrossRef]
  9. Taquet, M.; Holmes, E.A.; Harrison, P.J. Depression and Anxiety Disorders during the COVID-19 Pandemic: Knowns and Unknowns. Lancet 2021, 398, 1665–1666. [Google Scholar] [CrossRef]
  10. Gianfredi, V.; Provenzano, S.; Santangelo, O.E. What Can Internet Users’ Behaviours Reveal about the Mental Health Impacts of the COVID-19 Pandemic? A Systematic Review. Public Health 2021, 198, 44–52. [Google Scholar] [CrossRef]
  11. Wiwatkunupakarn, N.; Pateekhum, C.; Aramrat, C.; Jirapornchaoren, W.; Pinyopornpanish, K.; Angkurawaranon, C. Social Networking Site Usage: A Systematic Review of Its Relationship with Social Isolation, Loneliness, and Depression among Older Adults. Aging Ment. Health 2022, 26, 1318–1326. [Google Scholar] [CrossRef]
  12. Pietrabissa, G.; Simpson, S.G. Psychological Consequences of Social Isolation during COVID-19 Outbreak. Front. Psychol. 2020, 11, 2201. [Google Scholar] [CrossRef]
  13. Holt-Lunstad, J.; Smith, T.B.; Baker, M.; Harris, T.; Stephenson, D. Loneliness and Social Isolation as Risk Factors for Mortality: A Meta-Analytic Review. Perspect. Psychol. Sci. 2015, 10, 227–237. [Google Scholar] [CrossRef] [PubMed]
  14. Kawachi, I.; Berkman, L.F. Social Ties and Mental Health. J. Urban Health 2001, 78, 458–467. [Google Scholar] [CrossRef] [PubMed]
  15. Jose, P.E.; Lim, B.T.L. Social Connectedness Predicts Lower Loneliness and Depressive Symptoms over Time in Adolescents. Open J. Depress. 2014, 3, 49192. [Google Scholar] [CrossRef]
  16. Cacioppo, J.T.; Hawkley, L.C.; Norman, G.J.; Berntson, G.G. Social Isolation: Social Isolation. Ann. N. Y. Acad. Sci. 2011, 1231, 17–22. [Google Scholar] [CrossRef]
  17. Chao, S.F. Assessing Social Support and Depressive Symptoms in Older Chinese Adults: A Longitudinal Perspective. Aging Ment. Health 2011, 15, 765–774. [Google Scholar] [CrossRef]
  18. Levula, A.; Harré, M.; Wilson, A. The Association Between Social Network Factors with Depression and Anxiety at Different Life Stages. Community Ment. Health J. 2018, 54, 842–854. [Google Scholar] [CrossRef]
  19. Borgatti, S.P.; Mehra, A.; Brass, D.J.; Labianca, G. Network Analysis in the Social Sciences. Science 2009, 323, 892–895. [Google Scholar] [CrossRef]
  20. Schmälzle, R.; Brook O’Donnell, M.; Garcia, J.O.; Cascio, C.N.; Bayer, J.; Bassett, D.S.; Vettel, J.M.; Falk, E.B. Brain Connectivity Dynamics during Social Interaction Reflect Social Network Structure. Proc. Natl. Acad. Sci. USA 2017, 114, 5153–5158. [Google Scholar] [CrossRef]
  21. Rosenquist, J.N.; Fowler, J.H.; Christakis, N.A. Social Network Determinants of Depression. Mol. Psychiatry 2011, 16, 273–281. [Google Scholar] [CrossRef]
  22. Elmer, T.; Stadtfeld, C. Depressive Symptoms Are Associated with Social Isolation in Face-to-Face Interaction Networks. Sci. Rep. 2020, 10, 1444. [Google Scholar] [CrossRef]
  23. Ojagbemi, A.; Gureje, O. Typology of Social Network Structures and Late-Life Depression in Low- and Middle-Income Countries. Clin. Pract. Epidemiol. Ment. Health 2019, 15, 134–142. [Google Scholar] [CrossRef] [PubMed]
  24. Lau, Y.W.; Vaingankar, J.A.; Abdin, E.; Shafie, S.; Jeyagurunathan, A.; Zhang, Y.; Magadi, H.; Ng, L.L.; Chong, S.A.; Subramaniam, M. Social Support Network Typologies and Their Association with Dementia and Depression among Older Adults in Singapore: A Cross-Sectional Analysis. BMJ Open 2019, 9, e025303. [Google Scholar] [CrossRef] [PubMed]
  25. Werner-Seidler, A.; Afzali, M.H.; Chapman, C.; Sunderland, M.; Slade, T. The Relationship between Social Support Networks and Depression in the 2007 National Survey of Mental Health and Well-Being. Soc. Psychiatry Psychiatr. Epidemiol. 2017, 52, 1463–1473. [Google Scholar] [CrossRef] [PubMed]
  26. van den Brink, R.H.S.; Schutter, N.; Hanssen, D.J.C.; Elzinga, B.M.; Rabeling-Keus, I.M.; Stek, M.L.; Comijs, H.C.; Penninx, B.W.J.H.; Oude Voshaar, R.C. Prognostic Significance of Social Network, Social Support and Loneliness for Course of Major Depressive Disorder in Adulthood and Old Age. Epidemiol. Psychiatr. Sci. 2018, 27, 266–277. [Google Scholar] [CrossRef] [PubMed]
  27. Brummett, B.H.; Mark, D.B.; Siegler, I.C.; Williams, R.B.; Babyak, M.A.; Clapp-Channing, N.E.; Barefoot, J.C. Perceived Social Support as a Predictor of Mortality in Coronary Patients: Effects of Smoking, Sedentary Behavior, and Depressive Symptoms. Psychosom. Med. 2005, 67, 40–45. [Google Scholar] [CrossRef]
  28. Glanz, K.; Rimer, B.K.; Viswanath, K. (Eds.) Health Behavior: Theory, Research, and Practice, 5th ed.; Jossey-Bass Public Health; Jossey-Bass: San Francisco, CA, USA, 2015; ISBN 978-1-118-62898-0. [Google Scholar]
  29. Devassy, S.M.; Scaria, L.; Cheguvera, N.; Thampi, K. Association of Depression and Anxiety with Social Network Types: Results from a Community Cohort Study. Int. J. Environ. Res. Public Health 2021, 18, 6120. [Google Scholar] [CrossRef]
  30. Monninger, M.; Aggensteiner, P.-M.; Pollok, T.M.; Reinhard, I.; Hall, A.S.M.; Zillich, L.; Streit, F.; Witt, S.-H.; Reichert, M.; Ebner-Priemer, U.; et al. Real-Time Individual Benefit from Social Interactions before and during the Lockdown: The Crucial Role of Personality, Neurobiology and Genes. Transl. Psychiatry 2022, 12, 28. [Google Scholar] [CrossRef]
  31. Wu, T.; Jia, X.; Shi, H.; Niu, J.; Yin, X.; Xie, J.; Wang, X. Prevalence of Mental Health Problems during the COVID-19 Pandemic: A Systematic Review and Meta-Analysis. J. Affect. Disord. 2021, 281, 91–98. [Google Scholar] [CrossRef]
  32. Catalini, A.; Mazza, C.; Cosma, C.; Minutolo, G.; De Nicolò, V.; Gallinoro, V.; Caminiti, M.; Ancona, A.; Stacchini, L.; Berselli, N.; et al. Public Health Residents’ Anonymous Survey in Italy (PHRASI): Study Protocol for a Cross-Sectional Study for a Multidimensional Assessment of Mental Health and Its Determinants. Int. J. Environ. Res. Public Health 2023, 20, 2003. [Google Scholar] [CrossRef]
  33. Charan, J.; Biswas, T. How to Calculate Sample Size for Different Study Designs in Medical Research? Indian J. Psychol. Med. 2013, 35, 121–126. [Google Scholar] [CrossRef]
  34. Silvestri, C.; Carpita, B.; Cassioli, E.; Lazzeretti, M.; Rossi, E.; Messina, V.; Castellini, G.; Ricca, V.; Dell’Osso, L.; Bolognesi, S.; et al. Prevalence Study of Mental Disorders in an Italian Region. Preliminary Report. BMC Psychiatry 2023, 23, 12. [Google Scholar] [CrossRef] [PubMed]
  35. Menichini, D.; Fanetti, O.; Molinazzi, M.T. Physical Activity in Low Risk Pregnant Women: A Cross-Sectional Study. Clin. Ter. 2020, 171, e328–e334. [Google Scholar] [CrossRef] [PubMed]
  36. Vincenty, T. Direct and inverse solutions of geodesics on the ellipsoid with application of nested equations. Surv. Rev. 1975, 23, 88–93. [Google Scholar] [CrossRef]
  37. Topp, C.W.; Østergaard, S.D.; Søndergaard, S.; Bech, P. The WHO-5 Well-Being Index: A Systematic Review of the Literature. Psychother. Psychosom. 2015, 84, 167–176. [Google Scholar] [CrossRef]
  38. Mazzotti, E.; Fassone, G.; Picardi, A.; Sagoni, E.; Ramieri, L.; Lega, I.; Camaioni, D.; Abeni, D.; Pasquini, P. Il Patient Health Questionnaire (PHQ) per Lo Screening Dei Disturbi Psichiatrici: Uno Studio Di Validazione Nei Confronti Della Intervista Clinica Strutturata per Il DSM-IV Asse I (SCID-I) (The Patient Health Questionnaire (PHQ) for the Screening of Psychiatric Disorders: A Validation Study versus the Structured Clinical Interview for DSM-IV Axis I (SCID-I)). Ital. J. Psychopatol. 2003, 9, 235–242. [Google Scholar]
  39. Janssen, E.P.C.J.; Köhler, S.; Stehouwer, C.D.A.; Schaper, N.C.; Dagnelie, P.C.; Sep, S.J.S.; Henry, R.M.A.; Van Der Kallen, C.J.H.; Verhey, F.R.; Schram, M.T. The Patient Health Questionnaire-9 as a Screening Tool for Depression in Individuals with Type 2 Diabetes Mellitus: The Maastricht Study. J. Am. Geriatr. Soc. 2016, 64, e201–e206. [Google Scholar] [CrossRef]
  40. Bush, K. The AUDIT Alcohol Consumption Questions (AUDIT-C)An Effective Brief Screening Test for Problem Drinking. Arch. Intern. Med. 1998, 158, 1789. [Google Scholar] [CrossRef]
  41. Campbell, C.E.; Maisto, S.A. Validity of the AUDIT-C Screen for at-Risk Drinking among Students Utilizing University Primary Care. J. Am. Coll. Health 2018, 66, 774–782. [Google Scholar] [CrossRef]
  42. Mannocci, A.; Masala, D.; Mei, D.; Tribuzio, A.M.; Villari, P.; LA Torre, G. International Physical Activity Questionnaire for Adolescents (IPAQ A): Reliability of an Italian Version. Minerva Pediatr (Torino). Minerva Pediatr. 2021, 73, 383–390. [Google Scholar] [CrossRef]
  43. Schaeffer, M.S.; Levitt, E.E. Concerning Kendall’s Tau, a Nonparametric Correlation Coefficient. Psychol. Bull. 1956, 53, 338–346. [Google Scholar] [CrossRef]
  44. European Union. Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the Protection of Natural Persons with Regard to the Processing of Personal Data and on the Free Movement of Such Data, and Repealing Directive 95/46/EC (General Data Protection Regulation). Eur-Lex. Access to European Unione Law. 2016. Available online: https://eur-lex.europa.eu/eli/reg/2016/679/oj (accessed on 5 September 2023).
  45. Italia. Decreto Legislativo 10 Agosto 2018, n. 101. Disposizioni per L’adeguamento Della Normativa Nazionale Alle Disposizioni del Regolamento (UE) 2016/679 del Parlamento Europeo e del Consiglio, del 27 Aprile 2016, Relativo Alla Protezione Delle Persone Fisiche Con Riguardo al Trattamento dei Dati Personali, Nonché alla Libera Circolazione di Tali Dati e Che Abroga la Direttiva 95/46/CE (Regolamento Generale Sulla Protezione Dei Dati). Gazzetta Ufficiale Della Repubblica Italiana Serie Generale n. 159 del 4 September 2018. Available online: https://www.gazzettaufficiale.it/eli/id/2018/09/04/18G00129/sg (accessed on 5 September 2023).
  46. Italia. Decreto Legislativo 30 Giugno 2003, n. 196. Codice in Materia di Protezione dei Dati Personali. In Ministero della Giustizia—Ufficio Pubblicazioni Leggi e Decreti, Editor. Supplemento Ordinario alla “Gazzetta Ufficiale della Repubblica Italiana”; Serie Generale; Istituto Poligrafico e Zecca Dello Stato: Roma, Italy, 2003; Volume 123/L, pp. 11–207. Available online: https://www.gazzettaufficiale.it/eli/gu/2003/07/29/174/so/123/sg/pdf (accessed on 5 September 2023).
  47. Thoits, P.A. Mechanisms Linking Social Ties and Support to Physical and Mental Health. J. Health Soc. Behav. 2011, 52, 145–161. [Google Scholar] [CrossRef] [PubMed]
  48. Holt-Lunstad, J.; Smith, T.B.; Layton, J.B. Social Relationships and Mortality Risk: A Meta-Analytic Review. PLoS Med. 2010, 7, e1000316. [Google Scholar] [CrossRef] [PubMed]
  49. Cacioppo, J.T.; Hughes, M.E.; Waite, L.J.; Hawkley, L.C.; Thisted, R.A. Loneliness as a Specific Risk Factor for Depressive Symptoms: Cross-Sectional and Longitudinal Analyses. Psychol. Aging 2006, 21, 140–151. [Google Scholar] [CrossRef] [PubMed]
  50. Gianfredi, V.; Beran, M.; Koster, A.; Eussen, S.J.; Odone, A.; Signorelli, C.; Schaper, N.C.; Köhler, S.; Bosma, H.; Dagnelie, P.C.; et al. Association between social network characteristics and prevalent and incident depression: The Maastricht Study. J. Affect. Disord. 2021, 293, 338–346. [Google Scholar] [CrossRef] [PubMed]
  51. Gariépy, G.; Honkaniemi, H.; Quesnel-Vallée, A. Social Support and Protection from Depression: Systematic Review of Current Findings in Western Countries. Br. J. Psychiatry 2016, 209, 284–293. [Google Scholar] [CrossRef] [PubMed]
  52. Simmons, M.B.; Cartner, S.; MacDonald, R.; Whitson, S.; Bailey, A.; Brown, E. The Effectiveness of Peer Support from a Person with Lived Experience of Mental Health Challenges for Young People with Anxiety and Depression: A Systematic Review. BMC Psychiatry 2023, 23, 194. [Google Scholar] [CrossRef]
  53. Richard, J.; Rebinsky, R.; Suresh, R.; Kubic, S.; Carter, A.; Cunningham, J.E.A.; Ker, A.; Williams, K.; Sorin, M. Scoping Review to Evaluate the Effects of Peer Support on the Mental Health of Young Adults. BMJ Open 2022, 12, e061336. [Google Scholar] [CrossRef]
  54. Salk, R.H.; Hyde, J.S.; Abramson, L.Y. Gender Differences in Depression in Representative National Samples: Meta-Analyses of Diagnoses and Symptoms. Psychol. Bull. 2017, 143, 783–822. [Google Scholar] [CrossRef]
  55. Abraham, A.; Chaabna, K.; Doraiswamy, S.; Bhagat, S.; Sheikh, J.; Mamtani, R.; Cheema, S. Depression among Healthcare Workers in the Eastern Mediterranean Region: A Systematic Review and Meta-Analysis. Hum. Resour. Health 2021, 19, 81. [Google Scholar] [CrossRef]
  56. Iglesias Martínez, E.; Roces García, J.; Jiménez Arberas, E.; Llosa, J.A. Difference between Impacts of COVID-19 on Women and Men’s Psychological, Social, Vulnerable Work Situations, and Economic Well-Being. Int. J. Environ. Res. Public Health 2022, 19, 8849. [Google Scholar] [CrossRef]
  57. Gao, Y.-Q.; Pan, B.-C.; Sun, W.; Wu, H.; Wang, J.-N.; Wang, L. Depressive Symptoms among Chinese Nurses: Prevalence and the Associated Factors: Depressive Symptoms in Chinese Nurses. J. Adv. Nurs. 2012, 68, 1166–1175. [Google Scholar] [CrossRef] [PubMed]
  58. Naidoo, T.; Tomita, A.; Paruk, S. Burnout, Anxiety and Depression Risk in Medical Doctors Working in KwaZulu-Natal Province, South Africa: Evidence from a Multi-Site Study of Resource-Constrained Government Hospitals in a Generalised HIV Epidemic Setting. PLoS ONE 2020, 15, e0239753. [Google Scholar] [CrossRef] [PubMed]
  59. Dormann, C.; Zapf, D. Social Support, Social Stressors at Work, and Depressive Symptoms: Testing for Main and Moderating Effects with Structural Equations in a Three-Wave Longitudinal Study. J. Appl. Psychol. 1999, 84, 874–884. [Google Scholar] [CrossRef] [PubMed]
  60. Padkapayeva, K.; Gilbert-Ouimet, M.; Bielecky, A.; Ibrahim, S.; Mustard, C.; Brisson, C.; Smith, P. Gender/Sex Differences in the Relationship between Psychosocial Work Exposures and Work and Life Stress. Ann. Work. Expo. Health 2018, 62, 416–425. [Google Scholar] [CrossRef]
  61. Rugulies, R.; Bültmann, U.; Aust, B.; Burr, H. Psychosocial Work Environment and Incidence of Severe Depressive Symptoms: Prospective Findings from a 5-Year Follow-up of the Danish Work Environment Cohort Study. Am. J. Epidemiol. 2006, 163, 877–887. [Google Scholar] [CrossRef]
  62. Keohane, A.; Richardson, N. Negotiating Gender Norms to Support Men in Psychological Distress. Am. J. Mens. Health 2018, 12, 160–171. [Google Scholar] [CrossRef] [PubMed]
  63. Magnusson Hanson, L.L.M.H.; Leineweber, C.L.; Chungkham, H.S.; Westerlund, H. Work–Home Interference and Its Prospective Relation to Major Depression and Treatment with Antidepressants. Scand. J. Work Environ. Health 2014, 40, 66–73. [Google Scholar] [CrossRef]
  64. Wang, J.; Patten, S.B.; Currie, S.; Sareen, J.; Schmitz, N. A Population-Based Longitudinal Study on Work Environmental Factors and the Risk of Major Depressive Disorder. Am. J. Epidemiol. 2012, 176, 52–59. [Google Scholar] [CrossRef]
  65. Cerrato, J.; Cifre, E. Gender Inequality in Household Chores and Work-Family Conflict. Front. Psychol. 2018, 9, 1330. [Google Scholar] [CrossRef]
  66. Carcedo, R.J.; Fernández-Rouco, N.; Fernández-Fuertes, A.A.; Martínez-Álvarez, J.L. Association between Sexual Satisfaction and Depression and Anxiety in Adolescents and Young Adults. Int. J. Environ. Res. Public Health 2020, 17, 841. [Google Scholar] [CrossRef]
  67. Olson, J.S.; Crosnoe, R. Are You Still Bringing Me Down?: Romantic Involvement and Depressive Symptoms from Adolescence to Young Adulthood. J. Health Soc. Behav. 2017, 58, 102–115. [Google Scholar] [CrossRef] [PubMed]
  68. Vasilenko, S.A. Age-Varying Associations between Nonmarital Sexual Behavior and Depressive Symptoms across Adolescence and Young Adulthood. Dev. Psychol. 2017, 53, 366–378. [Google Scholar] [CrossRef] [PubMed]
  69. Gustavson, K.; Røysamb, E.; von Soest, T.; Helland, M.J.; Karevold, E.; Mathiesen, K.S. Reciprocal Longitudinal Associations between Depressive Symptoms and Romantic Partners’ Synchronized View of Relationship Quality. J. Soc. Pers. Relatsh. 2012, 29, 776–794. [Google Scholar] [CrossRef]
  70. Joosten, D.H.J.; Nelemans, S.A.; Meeus, W.; Branje, S. Longitudinal Associations between Depressive Symptoms and Quality of Romantic Relationships in Late Adolescence. J. Youth Adolesc. 2022, 51, 509–523. [Google Scholar] [CrossRef] [PubMed]
  71. Vujeva, H.M.; Furman, W. Depressive Symptoms and Romantic Relationship Qualities from Adolescence Through Emerging Adulthood: A Longitudinal Examination of Influences. J. Clin. Child Adolesc. Psychol. 2011, 40, 123–135. [Google Scholar] [CrossRef] [PubMed]
  72. Stice, E.; Ragan, J.; Randall, P. Prospective Relations Between Social Support and Depression: Differential Direction of Effects for Parent and Peer Support? J. Abnorm. Psychol. 2004, 113, 155–159. [Google Scholar] [CrossRef]
  73. Coyne, J.C.; Burchill, S.A.L.; Stiles, W.B. An Interactional Perspective on Depression. In Handbook of Social and Clinical Psychology: The Health Perspective; Pergamon General Psychology Series; Pergamon Press: Elmsford, NY, USA, 1991; Volume 162, pp. 327–349. ISBN 0-08-036128-5. [Google Scholar]
  74. Boop, C.; Cahill, S.M.; Davis, C.; Dorsey, J.; Gibbs, V.; Herr, B.; Kearney, K.; Metzger, L.; Miller, J.; Owens, A.; et al. Occupational Therapy Practice Framework: Domain and Process—Fourth Edition. Am. J. Occup. Ther. 2020, 74, 7412410010p1–7412410010p87. [Google Scholar] [CrossRef]
  75. Wang, J.; Mann, F.; Lloyd-Evans, B.; Ma, R.; Johnson, S. Associations between Loneliness and Perceived Social Support and Outcomes of Mental Health Problems: A Systematic Review. BMC Psychiatry 2018, 18, 156. [Google Scholar] [CrossRef]
  76. Weziak-Bialowolska, D.; Bialowolski, P. Bidirectional Associations between Meaning in Life and the Health, Emotional Ill-Being and Daily Life Functioning Outcomes among Older Adults. Psychol. Health 2022, 1–17. [Google Scholar] [CrossRef]
  77. Joules, N.; Williams, D.M.; Thompson, A.W. Depression in Resident Physicians: A Systematic Review. Open J. Depress. 2014, 3, 48621. [Google Scholar] [CrossRef]
  78. Cedrone, F.; Berselli, N.; Stacchini, L.; De Nicolò, V.; Caminiti, M.; Ancona, A.; Minutolo, G.; Mazza, C.; Cosma, C.; Gallinoro, V.; et al. Depressive Symptoms of Public Health Medical Residents during the COVID-19 Pandemic, a Nation-Wide Survey: The PHRASI Study. Int. J. Environ. Res. Public Health 2023, 20, 5620. [Google Scholar] [CrossRef] [PubMed]
  79. Lasalvia, A.; Bonetto, C.; Porru, S.; Carta, A.; Tardivo, S.; Bovo, C.; Ruggeri, M.; Amaddeo, F. Psychological Impact of COVID-19 Pandemic on Healthcare Workers in a Highly Burdened Area of North-East Italy. Epidemiol. Psychiatr. Sci. 2021, 30, e1. [Google Scholar] [CrossRef]
  80. Santini, Z.I.; Koyanagi, A.; Tyrovolas, S.; Mason, C.; Haro, J.M. The association between social relationships and depression: A systematic review. J. Affect. Disord. 2015, 175, 53–65. [Google Scholar] [CrossRef] [PubMed]
  81. Paulhus, D.L. Measurement and Control of Response Bias. In Measures of Personality and Social Psychological Attitudes; Measures of Social Psychological Attitudes; Academic Press: San Diego, CA, USA, 1991; Volume 1, pp. 17–59. ISBN 0-12-590241-7. [Google Scholar]
  82. Boruch, R.F. Assuring Confidentiality of Responses in Social Research: A Note on Strategies. Am. Sociol. 1971, 6, 308–311. [Google Scholar]
  83. Parker, G.; Brotchie, H. Gender Differences in Depression. Int. Rev. Psychiatry 2010, 22, 429–436. [Google Scholar] [CrossRef] [PubMed]
  84. Asher, M.; Asnaani, A.; Aderka, I.M. Gender Differences in Social Anxiety Disorder: A Review. Clin. Psychol. Rev. 2017, 56, 1–12. [Google Scholar] [CrossRef]
  85. Cedrone, F.; Catalini, A.; Stacchini, L.; Berselli, N.; Caminiti, M.; Mazza, C.; Cosma, C.; Minutolo, G.; Di Martino, G. The Role of Gender in the Association between Mental Health and Potentially Preventable Hospitalizations: A Single-Center Retrospective Observational Study. Int. J. Environ. Res. Public Health 2022, 19, 14691. [Google Scholar] [CrossRef]
Behavsci 13 00881 g001
Figure 1. Multicollinearity model analysis.
Figure 1. Multicollinearity model analysis.
Behavsci 13 00881 g001
Table 1. Relationships between socio-demographic characteristics and mild-to-severe (PHQ ≥ 5) and clinically relevant depressive symptoms (PHQ-9 ≥ 10).
Table 1. Relationships between socio-demographic characteristics and mild-to-severe (PHQ ≥ 5) and clinically relevant depressive symptoms (PHQ-9 ≥ 10).
CharacteristicOverallPHQ-9 Score ≥ 5PHQ-9 Score ≥ 10
n = 379No (n = 148)Yes (n = 231)pNo (n = 282)Yes (n = 97)p
Sex 0.453 1 0.802 1
Female219 (57.78%)82 (55.41%)137 (59.31%) 164 (58.16%)55 (56.70%)
Male160 (42.22%)66 (44.59%)94 (40.69%) 118 (41.84%)42 (43.30%)
Age30.00 (29.00, 34.00)30.00 (28.00, 34.00)31.00 (29.00, 33.00)0.446 230.00 (28.00, 34.00)31.00 (29.00, 33.00)0.134 2
Functional characteristics of social network
Peer-to-Peer Support4.00 (3.00, 5.00)4.00 (4.00, 5.00)4.00 (3.00, 5.00)<0.001 24.00 (4.00, 5.00)4.00 (3.00, 4.00)0.031 2
Supervisor Support4.00 (3.00, 4.00)4.00 (3.00, 5.00)3.00 (3.00, 4.00)<0.001 24.00 (3.00, 4.00)3.00 (2.00, 4.00)<0.001 2
WLI3.00 (2.00, 3.00)2.00 (2.00, 3.00)3.00 (2.00, 3.00)<0.001 22.00 (2.00, 3.00)3.00 (2.00, 4.00)<0.001 2
Structural characteristics of social network
Distance0.00 (0.00, 122.29.0)0.00 (0.00, 201.27)0.00 (0.00, 0.00)0.026 20.00 (0.00, 137.52)0.00 (0.00, 111.21)0.786 2
Family Members2.00 (2.00, 2.00)2.00 (2.00, 2.00)2.00 (2.00, 2.00)0.870 22.00 (2.00, 2.00)2.00 (2.00, 2.00)0.124 2
Having a partner 0.141 1 0.043 1
Yes276 (72.82%)114 (77.03%)162 (70.13%)213 (75.53%)34 (35.05%)
No103 (27.18%)34 (22.97%69 (29.87%)69 (24.47%)63 (64.95%)
Cohabitation 0.760 1 0.433 1
Living Alone98 (25.86%)37 (25.00%)61 (26.41%)70 (25.0%)28 (29.0%)
With Others281 (74.14%)111 (75.00%)170 (73.59%)212 (75.0%)69 (71.0%)
Social participation3.00 (2.00, 3.00)3.00 (3.00, 4.00)2.00 (1.00, 3.00)<0.001 23.0 (2.0, 4.0)1.0 (1.0, 2.0)<0.001 2
PHQ-9: 9-item Patient Health Questionnaire; WLI: work-to-private life interference; 1 Pearson’s chi-squared test; 2 Wilcoxon rank sum test.
Table 2. Association between functional and structural characteristics of social networks and depressive symptoms calculated using multivariate linear regression models adjusted for age and sex.
Table 2. Association between functional and structural characteristics of social networks and depressive symptoms calculated using multivariate linear regression models adjusted for age and sex.
Characteristicaβ95% CI 1p
Functional characteristics of social network
Peer-to-peer support−1.01−1.52; −0.50<0.001
Supervisor support−1.20−1.67; −0.74<0.001
Work-to-private life interference (WLI)1.240.74; 1.74<0.001
Structural characteristics of social network
Distance0.000.00; 0.000.636
Family members−0.81−1.54; −0.080.031
Having a partner (ref. = No)−1.42−2.58; −0.250.017
Cohabitation (ref. = Alone)−1.13−2.32; 0.060.062
Social participation−2.13−2.49; −1.76<0.001
aβ: adjusted beta; CI: confidence interval; 1 Adjustment: age and sex.
Table 3. Logistic regression models assessing the association between social network characteristics and clinically relevant depressive symptoms (PHQ-9 ≥ 10).
Table 3. Logistic regression models assessing the association between social network characteristics and clinically relevant depressive symptoms (PHQ-9 ≥ 10).
PHQ-9 Score ≥ 10PHQ-9 Score ≥ 10
Sensitivity Analysis
CharacteristicaOR 195% CIpaOR 295% CIp
Functional characteristics of social network
Peer-to-peer support0.720.58; 0.900.0040.620.47; 0.830.001
Supervisor support0.690.55; 0.85<0.0010.490.36; 0.68<0.001
Work-to-private-life interference (WLI)1.591.25; 2.01<0.0011.771.28; 2.45<0.001
Structural characteristics of social network
Distance1.001.00; 1.000.8951.001.00; 1.000.816
Family Members0.730.51; 1.030.0750.730.51; 1.050.089
Having a partner (ref. = No)0.590.36; 0.970.0390.490.25; 0.960.036
Cohabitation (ref. = Alone)0.820.49; 1.370.4430.820.48; 1.400.466
Social participation0.390.31; 0.50<0.0010.380.29; 0.49<0.001
aOR: adjusted odds ratio; 1 Adjustment: age and sex; 2 Adjustment: age, sex, drinking risk, and physical activity.
Table 4. Relationships between socio-demographic characteristics and depression symptoms and moderation by sex.
Table 4. Relationships between socio-demographic characteristics and depression symptoms and moderation by sex.
CharacteristicModeratorPHQ Score ≥ 10
aOR 195% CIp
Functional characteristics of social network
Peer-to-peer supportPredictor0.630.48; 0.840.002
Interaction1.420.89; 2.270.142
Supervisor supportPredictor0.520.38; 0.71<0.001
Interaction1.771.11; 2.740.010
Work-to-private-life interference (WLI)Predictor1.741.27; 2.38<0.001
Interaction0.810.50; 1.300.375
Structural characteristics of social network
DistancePredictor1.001.00; 1.000.757
Interaction1.001.00; 1.000.761
Family MembersPredictor0.800.55; 1.160.239
Interaction1.020.55; 1.880.959
Having a partner (ref. = No)Predictor0.470.25; 0.900.023
Interaction1.740.63; 4.860.289
Cohabitation (ref. = Alone)Predictor0.690.34; 1.400.310
Interaction1.420.50; 4.000.513
Social participationPredictor0.370.26; 0.51<0.001
Interaction1.160.71; 1.900.555
1 Adjustment: age and sex.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Stacchini, L.; Catalini, A.; De Nicolò, V.; Cosma, C.; Gallinoro, V.; Ancona, A.; Berselli, N.; Caminiti, M.; Mazza, C.; Minutolo, G.; et al. Social Network Characteristics and Depressive Symptoms of Italian Public Health Medical Residents: The Public Health Residents’ Anonymous Survey in Italy (PHRASI). Behav. Sci. 2023, 13, 881. https://doi.org/10.3390/bs13110881

AMA Style

Stacchini L, Catalini A, De Nicolò V, Cosma C, Gallinoro V, Ancona A, Berselli N, Caminiti M, Mazza C, Minutolo G, et al. Social Network Characteristics and Depressive Symptoms of Italian Public Health Medical Residents: The Public Health Residents’ Anonymous Survey in Italy (PHRASI). Behavioral Sciences. 2023; 13(11):881. https://doi.org/10.3390/bs13110881

Chicago/Turabian Style

Stacchini, Lorenzo, Alessandro Catalini, Valentina De Nicolò, Claudia Cosma, Veronica Gallinoro, Angela Ancona, Nausicaa Berselli, Marta Caminiti, Clara Mazza, Giuseppa Minutolo, and et al. 2023. "Social Network Characteristics and Depressive Symptoms of Italian Public Health Medical Residents: The Public Health Residents’ Anonymous Survey in Italy (PHRASI)" Behavioral Sciences 13, no. 11: 881. https://doi.org/10.3390/bs13110881

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop