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Article

Personality Traits and Sexual Attitudes as Predictors of Risky Sexual Behaviors in Health Science Students

by
María Naranjo-Márquez
1,
Anna Bocchino
1,*,
Ester Gilart
2,
Eva Manuela Cotobal-Calvo
1,
Concepción Mata-Pérez
1 and
José Luis Palazón-Fernández
1
1
Salus Infirmorum Nursing Center, University of Cádiz, 11001 Cádiz, Spain
2
Department of Biomedicine, Biotechnology and Public Health, University of Cádiz, 11001 Cádiz, Spain
*
Author to whom correspondence should be addressed.
Youth 2026, 6(1), 19; https://doi.org/10.3390/youth6010019
Submission received: 18 November 2025 / Revised: 3 February 2026 / Accepted: 3 February 2026 / Published: 7 February 2026
Versions Notes

Abstract

Previous research suggests that certain personality traits, such as extraversion and openness, may be associated with sexual risk behaviors (SRB). Exploring psychological and social factors is critical to guide effective sexual health promotion. Background/Objectives: To examine the associations between sociodemographic characteristics, personality traits, sexual attitudes, and risky sexual behaviors among health science students. Methods: A cross-sectional study was conducted with 730 health science students (median age: 21 years, IQR: 20–22 years) using validated questionnaires on personality (BPQ), sexual attitudes (BSAS), and sociodemographic factors. Non-parametric tests and logistic regressions were performed. Results: The results highlight significant differences in sexual attitudes based on sociodemographic characteristics, such as sexual orientation, type of relationship and cohabitation. High levels of pornography consumption are associated with drug use and greater permissiveness. A relationship was established between condom use and openness and intellect. Drug use scored high on extraversion and permissiveness. In relation to the use of dating apps, a positive relationship was found with permissiveness and substance use. Conclusions: The findings reveal a relationship between personality, sociodemographic characteristics, and risky behaviors, underscoring the need for tailored strategies in sexual health education for young adults.

1. Introduction

The sustained increase in the incidence of sexually transmitted infections (STIs)—such as Chlamydia trachomatis, lymphogranuloma venereum (LGV), syphilis, and gonorrhea—has prompted international health authorities to prioritize the eradication of HIV/AIDS and STIs as a key objective in the 2023 Millennium Development Agenda (World Health Organization, 2018). In response to this situation, the Spanish Ministry of Health developed the Strategic Plan for the Prevention and Control of HIV and other STIs, with the goal of reducing morbidity rates among the population aged 24 to 35 years (Ministerio de Sanidad, 2023; Ministerio de Sanidad, Servicios Sociales e Igualdad, 2015). According to scientific evidence, factors that have been identified as predisposing to STI transmission include drug use (Palacios & Álvarez, 2018), the use of dating apps (Queiroz et al., 2019; Xu et al., 2018), exposure to pornographic material (Luder et al., 2011; Braithwaite et al., 2015), multiple sexual partners (Li et al., 2012) and a lack of or inappropriate use of contraceptive methods (Desiré et al., 2023; García & Cardín, 2009). Likewise, the influence of psychological variables, such as personality traits (López-Cisneros et al., 2021; Alonso & Romero, 2019), and the level of knowledge about STIs (Thomas et al., 2022) has been highlighted, as these can play a significant role in the adoption of risky sexual behaviors.
The relationship between personality and risky sexual behavior is not linear, but rather the result of a highly complex relationship involving psychological, social and contextual determinants. Various studies have shown that certain personality traits can increase the predisposition to engage in unsafe sexual behavior (López-Cisneros et al., 2021; Alonso & Romero, 2019). From a theoretical construct, personality can be defined as a multidimensional and dynamic system of relatively stable traits that influence the way people perceive, interpret and cope with their everyday experiences, including those related to sexuality. In this sense, the Five-Factor Model (FFM) (John & Srivastava, 1999) has established itself as a conceptual framework for the study of individual differences. This model describes five dimensions, extraversion, agreeableness, conscientiousness, neuroticism, and openness to experience, which have been shown to be consistent predictors of a wide range of health-related behaviors, including those related to sexuality and risk-taking (Allen & Walter, 2018). Extraversion refers to sociability, assertiveness and a tendency toward positive affect; agreeableness involves cooperativeness and sensitivity to others; conscientiousness reflects organization, responsibility and self-discipline; neuroticism is associated with emotional instability and a tendency toward negative affect; and openness to experience reflects curiosity, creativity and a preference for novelty.
The scientific literature has pointed to a consistent association between certain personality traits and risky sexual behavior. In particular, extroversion and openness to experience tend to be linked to a higher probability of engaging in this type of behavior, while traits such as conscientiousness and agreeableness tend to play a protective role against them (Hudek-Knezevic et al., 2007; Vidal-Borrás & Hernández-González, 2017; Allen & Walter, 2018). Recent research delves deeper into this relationship by incorporating mediating variables such as impulsivity and emotional regulation, proposing a biopsychosocial model that integrates personality components, affective self-regulation, and sexual behavior (Scroggs & Rosenberger, 2023). Along these lines, it has been suggested that high levels of extroversion and openness could facilitate the use of social networks and new digital platforms for erotic purposes (Warter et al., 2021; Fernández-Fernández et al., 2018), while there is evidence of an inverse relationship between extroversion and sexual inhibition (Bártová et al., 2021). This association is consistent with the characteristics of extraversion, as individuals high in this trait tend to be more sociable, assertive, and comfortable in interpersonal contexts, which may reduce feelings of inhibition or restraint in sexual situations. Likewise, in adolescent populations, high levels of extraversion and low levels of conscientiousness and kindness have been found to be associated with behaviors such as sexting (Alonso & Romero, 2019; Omanyo, 2016; Marcela & Ruiz, 2019). Gámez-Guadix et al. (2017) demonstrated, in a complementary manner, that people who exchange sexually explicit content have higher scores on neuroticism and extraversion, but lower scores on conscientiousness and agreeableness. In line with these findings, Hoyle et al. (2000) found a possible relationship between neuroticism, impulsivity, and the tendency to have multiple sexual partners, with lower contraceptive use (Orcasita et al., 2018; Michelini et al., 2021). Conversely, high levels of conscientiousness and emotional stability are associated with greater adherence to condom use (Marcela & Ruiz, 2019). However, it should be noted that personality traits do not act in isolation, and that knowledge and attitudes toward STIs also play a crucial role. In this regard, it has been observed that people with limited knowledge about STIs tend to engage in risky sexual practices more frequently (Prince et al., 2023), while greater information about STIs and proper condom use is associated with safer sexual behaviors (Tapia-Aguirre et al., 2004). In fact, effective educational programs can produce significant changes in sexual attitudes and behaviors, reducing exposure to risk (Zyl-Cillié & Vries, 2024).
Along with psychological variables, scientific evidence has also highlighted the influence of sociodemographic factors on risky sexual behavior. Age, gender, alcohol, drug and/or pornography use/consumption, relationship status, and the use of dating apps are related to different patterns of sexual activity and condom use (Chen et al., 2025; Herbenick et al., 2020; Rosenfeld et al., 2019; Copen, 2017).
For example, younger age and male gender are associated with higher levels of sexual risk, while being in a stable relationship or having greater sexual experience predicts safer practices. The inclusion of these variables in the present study allows for a more comprehensive analysis of the factors that influence the sexual behavior of university students. Despite the existence of multiple studies on the risk factors associated with STIs, the relationship between personality traits, sexual attitudes and risk behaviors remains an insufficiently explored area in the scientific literature, especially in the Spanish context. Recent research highlights the need to delve deeper into the psychological and social factors underlying these behaviors, given their relevance for the design of sexual health prevention and promotion programs (Naranjo-Márquez et al., 2025).
Therefore, the aim of this study was to analyze the associations between sociodemographic variables, personality traits, and sexual attitudes and four distinct risky sexual behaviors—condom use, drug use, dating app use, and pornography consumption—among health science students.
The specific objectives were as follows:
  • To describe the sociodemographic characteristics, risky sexual behaviors, personality traits, and sexual attitudes of the participants.
  • To examine the associations between sociodemographic characteristics, personality traits, sexual attitudes and four specific risky sexual behaviors (inconsistent condom use, substance use, dating app use, and pornography consumption) among health science students.
  • To analyze, using separate multivariable logistic regression models, the associations between each of the four risky sexual behaviors (inconsistent condom use, substance use, dating app use, and pornography consumption) were considered as dependent variables, and sociodemographic characteristics, personality traits and dimensions of sexual attitudes were considered as independent variables.

2. Materials and Methods

2.1. Participants

Using the GPower 3.1 program, a minimum sample size of n = 252 was calculated for an effect size of 0.25, alpha = 0.05 and a power of 0.95. The reference population consisted of students enrolled in health science degree programs at the University of Cádiz and its affiliated centers in the province of Cádiz (Spain). Nonetheless, an attempt was made to recruit as many students as possible, so the final sample included 730 participants.

2.2. Instruments

We used the following three instruments:
Ad hoc demographic and behavioral questionnaire: this contained questions related to age, gender, health science program course and academic year level, sexual orientation, history of sexually transmitted diseases, marital status, use of dating apps and pornographic material, religion, drugs and alcohol consumption and use of contraceptive methods.
Brief Personality Questionnaire (BPQ, Torreblanca Murillo, 2017): this is an original Spanish version of an instrument for assessing personality, consisting of 20 items rated on a 5-point Likert scale (1 = completely false; 5 = completely true). It assesses the five major personality factors: I, extraversion (items 1, 6, 11, 16); II, agreeableness (items 2, 7, 12, 17); III, conscientiousness (items 3, 8, 13, 18); IV, neuroticism (items 4, 9, 14, 19); and V, openness/intellect (items 5, 10, 15, 20). The reliability, measured by Cronbach’s alpha, ranges from 0.61 to 0.79 for all dimensions.
Brief Sexual Attitudes Scale (BSAS, Hendrick et al., 2006): this scale is composed of 23 items in a 5-point Likert format, organized into four dimensions: permissiveness (items 1 to 10, e.g., “Casual sex is acceptable”); Cronbach’s α = 0.93, birth control (items 11 to 13, e.g., “Birth control is part of responsible sexuality”); Cronbach’s α = 0.84, communion (items 14 to 18, e.g., “Sex is the closest form of communication between two people”); Cronbach’s α = 0.71, and instrumentality (items: 19 to 23, e.g., “Sex is primarily physical”), Cronbach’s α = 0.77. Higher scores indicate greater agreement with the attitudes described in each dimension.

2.3. Procedure

This cross-sectional observational study was conducted using convenience sampling. Data collection was carried out using an online questionnaire administered via Google Forms. Before the study began, the participating centers and the faculty members responsible were contacted to request the necessary authorizations and coordinate the distribution of the questionnaire among the students.
Before accessing the questionnaire, students received information explaining the objectives of the research, the voluntary nature of participation, and the possibility of withdrawing from the study at any time without consequences. Completion and submission of the questionnaire was interpreted as acceptance to participate. The estimated time to complete the sociodemographic questions and scales included was approximately 30 min.
The study complied with the ethical principles of the Declaration of Helsinki (2013 revision, Fortaleza) and Organic Law 3/2018 of December 5 on the protection of personal data and the guarantee of digital rights in Spain. The data were collected anonymously and treated confidentially, with access restricted exclusively to the research team. The study was conducted with the institutional approval of the participating centers.

2.4. Data Analysis

According to the Kolmogorov–Smirnov test, the data from quantitative variables (age, BPQ and BSAS) did not follow a normal distribution, so the statistical analyses were made using non-parametric methods. Quantitative variables were expressed as medians and interquartile ranges. Categorical variables were described as frequencies and percentages.
The median scores of the personality and sexual attitude subscales according to the different sociodemographic variables were compared using the Mann–Whitney U test for two groups (e.g., sex, religion) or the Kruskal–Wallis test for three groups (sexual orientation). In case of obtaining significant differences between groups, these were evaluated using the Dunn–Bonferroni test. In addition to statistical significance, effect size was calculated to quantify the magnitude of the observed differences. The effect size was estimated using Rosenthal’s r, for comparisons with the Mann–Whitney U test and epsilon squared (ε2) in the case of Kruskal–Wallis comparisons which are recommended for nonparametric comparisons based on rank tests. Effect size values for Rosental’s r were interpreted according to J. Cohen’s (1988) guidelines, with values of approximately 0.10 indicating a small effect, 0.30 a medium effect, and 0.50 a large effect. In the case of ε2, values of approximately 0.01 indicated a small effect, 0.06 a medium effect and 0.14 as a large effect.
Possible correlations between quantitative measures were assessed using Spearman rank correlation coefficients.
Given the conceptual and empirical distinctiveness of the outcomes, four separate multivariable logistic regression models were estimated, one for each risky sexual behavior (inconsistent condom use, dating app use, substance use, and pornography consumption). Using the enter method, the models were adjusted to investigate the association of multiple independent variables—sociodemographic characteristics, personality traits, and sexual attitudes—with each outcome. Initially, bivariate analyses were conducted to explore the association between each independent variable and the outcome of interest. Only predictors with a p-value < 0.20 in the univariate analyses were considered for inclusion in the multivariable models. Variables with a significant coefficient (p < 0.05) were retained, and the corresponding odds ratios (ORs) and 95% confidence intervals (CIs) were reported. The collinearity assumption was assessed prior to model estimation.
The data were analyzed using the IBM SPSS version 26.0 software for Windows (IBM Corp., Armonk, NY, USA). For all tests, p-values ≤ 0.05 were considered significant.

3. Results

3.1. Demographic and Behavioral Characteristics

The median age of the participants was 21 years (IQR 20–22 years), with 71% of the participants being female. The majority of the participants (84.4%) identified themselves as heterosexual, 4.4% as gay or lesbian, 9.3% as bisexual and 1.4% as asexual. Concerning religion, 72.2% reported being religious. More than half of the participants (56.9%) reported having a partner, although only 9.7% lived with their partner. Of those involved in a relationship, 95.8% were in exclusive or traditional relationships, whereas 4.2% reported being in an open relationship. Regarding the use of contraception, dating apps, drugs and pornography, 80.1% used contraception, 21.1% used dating apps, 59.6% consumed drugs and 34.9% consumed pornographic material.

3.2. Personality

The results for the different personality dimensions are presented in Table 1.

3.2.1. Extraversion

Extraversion scores ranged between 0 and 16 (Me = 9, IQR 7–11). Significant differences (p ≤ 0.05) were observed in the extraversion dimension scores according to gender, use of dating apps and consumption of pornographic material and drugs; however, the effect sizes were small (0.09–0.20). The highest scores were observed in men, those using dating apps and those consuming pornographic material and drugs.

3.2.2. Agreeableness

Agreeableness scores varied between 1 and 16 (Me = 12, IQR 10–14). Significant differences (p ≤ 0.05) in agreeableness values were observed in relation to gender, religion, having a partner, type of relationship and drug use before or during sexual intercourse. Effect sizes were small, ranging from 0.11 to 0.18. The highest scores were observed in women, people with religious beliefs, those with a partner, those in an exclusive relationship and those who did not use drugs before or during sexual intercourse.

3.2.3. Conscientiousness

Scores of conscientiousness ranged from 0 to 16 (Me = 10, IQR 8–12). Significant differences (p ≤ 0.05) were found in the conscientiousness dimension scores with respect to gender, sexual orientation, religiosity, having a partner, type of relationship, history of sexually transmitted infections, dating app use, pornography and drug consumption, and drug use before or during sexual intercourse, with small effect sizes ranging from 0.08 to 0.18 in two-group comparisons, and 0.01 in the three-group comparison for sexual orientation. The highest scores were observed for women, heterosexual individuals, religious people, those who have a partner and an exclusive relationship, those who have not had STIs, do not use dating apps, and do not use drugs or pornography.

3.2.4. Neuroticism

Neuroticism scores varied between 0 and 16 (Me = 8, IQR 6–9). Significant differences (p ≤ 0.05) in neuroticism scores were observed between men and women and with contraceptive use. Effect sizes indicated small effects (0.15 and 0.10, respectively). Women and participants who did not use contraceptives had the highest scores on the neuroticism scale.

3.2.5. Openness/Intellect

Scores for openness/intellect varied between 0 and 16 (Me = 9, IQR 7–11). Although significant differences (p ≤ 0.05) were obtained in the scores for this dimension according to gender, sexual orientation, religion, cohabitation, contraceptive use, pornography and drug consumption, the magnitude of these differences was small (effect sizes ranging from 0.08 to 0.10 in two-group comparisons and 0.02 in the three-group comparison for sexual orientation). The highest scores were observed for men, bisexual individuals, non-religious, those who do not live with their partner, those who use contraceptives, and those who consume pornography and drugs.

3.3. Brief Sexual Attitudes Scale

The results for each of the dimensions of the sexual attitude questionnaire are shown in Table 2.

3.3.1. Permissiveness

Permissiveness scores ranged from 1 to 5 (Me = 2.9, IQR 2.3–3.3). Although significant differences (p ≤ 0.05) in permissiveness scores were observed for gender, sexual orientation, religion, having or not having a partner, type of relationship, history of STIs, use of dating apps, and pornography and drug consumption, the magnitude of these differences were small, with effect sizes ranging from 0.12 to 0.23 for two-group comparisons and 0.01 in three-group comparisons for sexual orientation. The highest scores on the permissiveness scale were observed for men, individuals who identify themselves as gay or lesbian and bisexual individuals, non-religious, those without a partner, those in an open relationship, those who had a history of sexually transmitted infections, used dating apps, consumed pornography and drugs, and consumed drugs before or during sexual intercourse.

3.3.2. Birth Control

Birth control scores varied between 1 and 5 (Me = 3.5, IQR 3–4). Significant differences (p ≤ 0.05) were found in the scores for the birth control dimension in relation to history of STIs, although with a small effect size (0.08). People who had or have had STIs had the highest scores.

3.3.3. Communion

Scores of this dimension of sexual attitude varied between 1 and 5 (Me = 3.6, IQR 3–4.2). The communion dimension only showed significant differences (p ≤ 0.05) with the use of dating apps; however, the effect size was small (0.08) Those that used dating apps showed slightly higher scores in this dimension than those who did not.

3.3.4. Instrumentality

Instrumentality scores ranged from 1 to 5 (Me = 2.6, IQR 2–3). Significant differences (p ≤ 0.05) were observed in the instrumentality dimension scores according to having or not having a partner, drug use and drug use before or during sexual intercourse, with small effect-sizes (0.10–0.14). Higher values were identified in people who do not have a partner and use drugs.

3.4. Bivariate Correlation Analysis

The correlations between age, personality traits and the dimension of sexual attitudes are presented in Table 3. Most of the correlations were non-significant. Significant correlations were very low (<0.20) or low (0.20–0.39) (Evans, 1996).
Personality traits showed very low or low correlation between them, with the highest correlations between agreeableness and extraversion and conscientiousness (both positive), and openness/intellect with extraversion (positive) and neuroticism (negative). All significant correlations between personality traits and sexual attitudes were very low (<0.20).
All dimensions of sexual attitudes were correlated between them, with higher correlations found between permissiveness and instrumentality (0.375) and communion and birth control (0.286).
Age was correlated only with the conscientiousness (positive) and openness/intellect (negative) dimensions of the personality.

3.5. Logistic Regression Models

The multivariate logistic regression models for condom use, drug use, pornography consumption and dating app use are shown in Table 4.

3.5.1. Logistic Regression Model for Condom Use

Table 4 shows the results of the logistic regression for condom use. Age, gender, sexual orientation, type of relationship, pornography consumption and the openness/intellect dimension of the personality had significant effects on condom use. Inverting the odds ratios for age indicated that when holding all other variables constant, for every year of increasing age, the odds of not using a condom increases 1.11 times. With relation to gender, the inverted odd ratio reveals that a man is 3.571 times more likely to use condoms than a woman. Inverted odds ratios for the dummy variables coding the effect of sexual orientation indicated that the odds of using condoms for heterosexuals were 2.89 times higher than for a gay or lesbian. The odds for the dummy variables coding the effect of type of relationship compares each type of relationship, except traditional, to the traditional relationship. For those not in a relationship, the odds of using condoms are 1.607 times those for individuals in a traditional relationship. With relation to pornography consumption, the inverted odds indicated that for those not consuming pornography, the odds of using condoms are 1.621 times higher than for those who consume it. The odds for the personality trait “openness/intellect” indicate that for each unit increase in the score of this subscale, there is a 1.09 increase in the odds of using a condom.

3.5.2. Logistic Regression Model for Drug Consumption

Table 4 shows the results of the logistic regression for drug consumption. Age, pornography consumption, and the permissiveness of sexual attitude and extraversion of the personality dimensions had significant effects on drug use.
Inverting the odds ratios for age indicated that when holding all other variables constant, for every year of increasing age, the odds of not using drugs increase by 1.10 times. With relation to pornography consumption, the odds of drug use for those consuming pornography are 2.659 times the odds of those that do not consume it. The odds for the dimension “permissiveness” of sexual attitude indicate that for each one-unit increase in this subscale, the odds of using drugs increased by 55.5%. The odds for the personality trait “extraversion” indicates that for each one-unit increase in this subscale, the odds of using drugs increase by 10.5%.

3.5.3. Logistic Regression Model for Pornography Consumption

Table 4 shows the results of the logistic regression for pornography consumption. Gender, drug consumption, use of dating apps and the permissiveness and instrumentality dimensions of the sexual attitudes test had significant effects on pornography consumption. In relation to gender, the inverted odds ratio reveals that a man is 9.260 times more likely to consume pornographic material than a woman.
The odds ratios for drug consumption indicated that, when participants consume drugs, the odds of pornography consumption increase by 2.419 times with relation to participants who do not consume drugs.
The odds ratios for dating apps use indicated that, when participants use dating apps, the odds of pornography consumption increase by 3.774 times.
The odds for the permissiveness dimension of sexual attitude indicate that each one-unit increase in this subscale was associated with a 47.8% increase in the odds of consuming pornographic material.
The inverted odds for the instrumentality dimension of sexual attitude indicate that each one-unit increase in this subscale was associated with 42.9% higher odds of not consuming pornographic material.

3.5.4. Logistic Regression Model for Dating App Use

Table 4 shows the results of the logistic regression for dating app use. The sexual orientation, the type of relationship, cohabitation as a couple, pornography consumption, religion and communion dimension of the Sexual Attitudes Scale had significant effects on dating app use.
Odds ratios for the effect of sexual orientation indicated that the odds of using dating apps were 12.296 times higher for gay/lesbian individuals and 2.727 times higher for bisexual individuals relative to heterosexual individuals.
The odds for the dummy variables coding the effect of the type of relationship compares each type of relationship (except traditional) to the traditional relationship. For the open relationship, the odds of using dating apps is 12.832 times higher than for a traditional relationship. For participants not involved in a relationship, the odds of using dating apps are 4104 times those of the participants involved in a traditional relationship.
The odds ratios related to cohabitation as a couple indicated that living with a partner was associated with higher odds of using dating apps (OR = 2.641).
With relation to pornography consumption, people who consume pornography have 3.505 times higher odds of using dating apps compared to those who do not consume it.
Inverting the odds ratios for religion indicated that when holding all other variables constant, the odds of not using dating apps increase by 1.894 times in religious people compared with non-religious people.
In the case of the communion dimension of sexual attitudes, the odds of using dating apps increase by 49.1% for each one-unit increase in that subscale.

4. Discussion

4.1. Influence of Sociodemographic Characteristics on Sexual Attitudes and Behavior

Our findings indicated significant differences in sexual attitudes as a function of socio-demographic variables such as sexual orientation, type of relationship and cohabitation. Specifically, bisexual individuals and participants who reported being in open relationships showed higher levels of permissiveness. These results are consistent with previous studies suggesting that sexual orientation and relationship type significantly influence sexual behaviors and attitudes (Blanc, 2023; Blanc et al., 2023). These differences emphasize the importance of considering these socio-demographic factors in the design of future sexual health interventions, as they may affect risk perception and sexual decision-making.

4.2. Personality Traits as Correlates of Sexual Attitudes and Behaviors

This study suggests that personality traits are associated with sexual attitudes and certain risky sexual behaviors; however, their specific contribution in multivariable models was limited when stronger sociodemographic and attitudinal predictors were included. Specifically, extraversion and openness were positively associated with permissiveness, which is consistent with previous studies linking these traits to lower sexual inhibition and a higher propensity for risk-taking behavior (Allen & Walter, 2018). In contrast, conscientiousness showed a negative association with permissiveness, suggesting that individuals with higher levels of self-control and self-discipline may be more likely to adopt less risky sexual practices and to use contraception (Allen & Walter, 2018; Lauriola & Weller, 2018).
These findings support the interpretation that sexual attitudes, particularly permissiveness, may function as more proximal predictors of risky behaviors, potentially mediating some of the influence of more distal personality traits.
In addition, previous studies have reported that higher levels of conscientiousness and agreeableness are associated with less permissive sexual attitudes and a lower likelihood of engaging in risky behaviors (Scroggs & Rosenberger, 2023). In line with this, the negative association observed between neuroticism and responsible sexual behavior in the present study suggests that students with higher levels of this trait may report a less consistent use of protective measures. However, these results must be interpreted with caution, as causal relationships cannot be established due to the cross-sectional design. Nevertheless, the findings suggest that individual differences related to personality may still be relevant when addressing sexual health behaviors in educational settings, particularly in interaction with attitudinal and contextual factors.

4.3. Associations Between Permissiveness, Pornography Consumption and Substance Use

Results indicated significant associations between pornography consumption, substance use, and sexual attitudes. Higher pornography consumption was associated with higher levels of permissiveness, whereas drug use—especially before or during sexual activity—was associated with the greater instrumentalization of sex (Scroggs & Rosenberger, 2023; Pérez et al., 2013; D. Cohen, 2023; Guerras et al., 2020; Leonangeli et al., 2021). These associations highlight the need to address the role of pornography and substances in sexual contexts to prevent risky practices and reduce sexually transmitted infections (STIs).
Along these lines, our findings indicate that permissive sexual attitudes are also linked to an increased likelihood of drug use. Previous studies indicate that those with more liberal attitudes towards sexuality tend to engage in risky behaviors (Tapia et al., 2012). The association observed between substance use and permissive sexual attitudes could be explained, at least in part, by certain behavioral traits, such as a tendency toward thrill seeking and lower inhibitory control. Such characteristics that are associated with both extroversion and sexual permissiveness, as already indicated by several authors (Elmquist et al., 2016; Leeman et al., 2019), suggest that these behaviors could be part of a broader pattern oriented toward exploration and participation in stimulating experiences.

4.4. Multivariable Predictors of Condom Use, Drug Use and Dating App Use

4.4.1. Condom Use

Condom use, considered a protective behavior, was influenced by age, gender, sexual orientation, relationship type, pornography consumption, and trait openness/intellect. Results showed that men, younger people and those who were not in traditional relationships were more likely to use condoms, which is consistent with previous studies (Rotermann & McKay, 2009; Rotermann & McKay, 2020; Casola et al., 2022; Macapagal et al., 2021). In addition, openness to new experiences is related to more positive attitudes toward safer sex and a greater willingness to talk about sexual health (McCrae & Costa, 1997).
Regarding sexual orientation, heterosexual people showed a higher propension of condom use compared to gay or lesbian people. Although some studies suggest that these differences may be related to variations in perceived risk or access to sex education (Mijas et al., 2021), our findings should be interpreted with caution, as the present study did not examine these contextual factors. Therefore, this association reflects a statistical pattern rather than an explanatory mechanism, and further research is needed to clarify the sociocultural dynamics underlying these differences. It was also observed that those who do not consume pornography were more likely to use condoms, which is consistent with studies evidencing the impact of pornography on the perception of safe sex (Stokłosa et al., 2021; Kumar et al., 2021; Yunengsih & Setiawan, 2021).

4.4.2. Drug Use

Drug use was influenced by age, pornography use, sexual permissiveness, and extraversion. First, for each additional year of age, the probability of not using drugs increased, which supports studies identifying a higher prevalence of experimental use in early adulthood. Previous research has highlighted a higher prevalence of experimental or recreational drug use in early adulthood, attributable to factors such as sensation seeking, social pressure or lower risk perception (Adouani et al., 2022).
Pornography consumption was associated with a significant increase in the probability of drug use, which supports findings suggesting that exposure to sexual content may motivate the search for immediate gratification (Naranjo-Márquez et al., 2025). Likewise, extraversion was consolidated as a clear predictor of substance use, in agreement with studies linking this trait with sensation seeking and addictive behaviors, both substance-related and behavioral, such as gambling or compulsive internet use (Zilberman et al., 2020; Kräplin et al., 2022; Lauriola & Weller, 2018; Caselles et al., 2010; Dash et al., 2023). However, this association should be interpreted with caution, as contextual factors and individual decisions also play an important role (Davies et al., 2024; Agrawal et al., 2004).
On the other hand, permissive sexual attitudes were also related to higher substance use, suggesting a greater general openness to risky behaviors. Previous research has indicated that people with more liberal attitudes toward sexuality tend to engage in a variety of risky behaviors, including drug use (Tapia et al., 2012). In addition, impulsivity—present in both extraversion and sexual permissiveness—has been associated with an increase in risky sexual behaviors, especially under the influence of substances (Elmquist et al., 2016; Leeman et al., 2019).

4.4.3. Use of Dating Apps

The use of dating apps showed relationships with multiple variables: non-heterosexual sexual orientation, non-traditional relationships, cohabitation, low religiosity, pornography consumption, and the dimensions of communion. Gay, lesbian or bisexual individuals were more likely to use them, possibly as a means of interaction in environments perceived as safer (Alarcón-Gutiérrez et al., 2022).
In addition, those in open or formal relationships also frequently used these apps (Flesia et al., 2021). Recent studies indicate that, in Spain, more than 50% of the users of these platforms already have a partner, which is evidence of a change in relational dynamics (EAE Business School, 2025; Bleize et al., 2024). Interpersonal communion was also related to greater use of apps, suggesting that, in certain cases, they are used to strengthen existing bonds or explore new connections (Çizmeci, 2017; Macapagal et al., 2016).
Moreover, our results suggest that increased pornography consumption may be correlated with greater use of dating apps, possibly due to its association with weakened commitment in romantic relationships, as indicated by Lambert et al. (2012).
Finally, an inverse association was found between religiosity and app use, with religious people being 1.89 times more likely not to use them.

4.4.4. Permissiveness, Pornography and Gender

Pornography consumption was associated with several variables, including gender, app use, and the dimensions of permissiveness and sexual instrumentality, among others.
As the literature indicates, men tend to consume more pornography, start consuming it at younger ages, and do so more frequently (Miller et al., 2020; Ndimele, 2020). In addition, frequent exposure to pornographic content has been documented to be linked to permissive attitudes toward sexual relationships, including extramarital affairs (Wright, 2022; Wright et al., 2024).
The sexual permissiveness dimension correlated with both pornography consumption and the likelihood of developing problematic sexual behaviors (Lewczuk et al., 2023. On the other hand, instrumentality showed a negative association; for example, those who view sexuality primarily as a means to obtain benefits or achieve external goals (e.g., boosting self-esteem, maintaining a relationship, or gaining recognition) showed a lower tendency to consume pornography (Levin et al., 2019). These findings should be interpreted within a broader framework that considers the interaction between personality traits, religious beliefs, and sociocultural context. Despite these contributions, the findings should be interpreted in light of several limitations, which are outlined in the following section.

5. Limitations

This study has several limitations that should be taken into account when interpreting the results. Although the statistical significance threshold (p ≤ 0.05) was retained, statistical significance does not necessarily imply practical relevance, particularly in large samples. Therefore, the small effect sizes that reached statistical significance should be interpreted with caution. First, the cross-sectional design prevents the establishment of causal relationships between the variables examined, such as personality traits and sexual behaviors. Future research would benefit from longitudinal designs that allow for the examination of the temporal evolution of these associations. Furthermore, the use of self-reported questionnaires may have generated potential biases such as social desirability, especially when dealing with sensitive topics such as sex life, substance use, or condom use frequency. In addition, due to the nature of the sample—composed exclusively of health science students—the generalization of the results to other populations with different educational, cultural, or personal contexts may be limited. Moreover, interpretations related to sexual orientation and religiosity should be approached with caution, as the study design does not allow for an in-depth exploration of the sociocultural mechanisms underlying these differences. Overgeneralizing these findings could lead to unintended stigma, and therefore such results should be interpreted as preliminary associations rather than group-based conclusions. Nor was there an exhaustive examination of certain sociocultural factors, such as prior sex education, which could influence risk perception and attitudes towards risky sexual behavior. Additionally, the use of brief instruments to assess the Big Five personality traits and sexual attitudes may have limited the ability to capture specific facets (e.g., self-discipline, impulsivity, or excitement seeking) that could be differentially related to risky sexual behaviors, which may partly explain the reduced predictive contribution of personality traits in the multivariable models. Moreover, risky sexual behaviors were operationalized as dichotomous variables, which does not reflect the frequency or intensity of such behaviors and may have reduced variability in the outcomes, limiting the detection of more nuanced associations with individual psychological traits. Finally, the study did not incorporate a mixed methodological approach integrating quantitative and qualitative techniques, which would have allowed for a more comprehensive and contextualized understanding of the phenomena analyzed, as well as a more accurate interpretation of the relationships between individual and social variables.

6. Conclusions

The results of this study suggest that risky sexual behaviors among health science students are influenced by a combination of personality traits, sexual attitudes, and sociodemographic factors. Specifically, higher levels of extraversion and sexual permissiveness were associated with an increased likelihood of drug use and pornography consumption, while openness/intellect was positively related to condom use, suggesting a more proactive approach to sexual health among students with greater openness to experience. In contrast, higher conscientiousness appeared to act as a protective factor, being associated with lower involvement in several risk behaviors. In addition, factors such as age, gender, sexual orientation, relationship type, religiosity, and the use of dating apps were significantly related to distinct behavioral patterns, highlighting the relevance of contextual and relational variables. Together, these results support the need for sexual health interventions that go beyond purely informational approaches and incorporate psychological and social profiles, allowing for more targeted and effective prevention strategies in university students.

Author Contributions

Conceptualization, M.N.-M. and A.B.; methodology, J.L.P.-F.; validation, A.B. and J.L.P.-F.; formal analysis, J.L.P.-F. and M.N.-M.; investigation, M.N.-M., C.M.-P., A.B. and E.G.; writing—original draft preparation, M.N.-M., A.B., E.M.C.-C. and J.L.P.-F.; writing—review and editing, M.N.-M., A.B. and J.L.P.-F.; supervision, A.B. and J.L.P.-F. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki 2013 and Spanish Organic Law 3/2018 of 5 December on the Protection of Personal Data and Guarantee of Digital Rights. The study did not involve clinical interventions, drug trials, or the collection of sensitive personal data, and all data were anonymized prior to analysis. In accordance with Royal Decree 1090/2015, which regulates clinical trials and Research Ethics Committees for Medicinal Products (CEIm), and Law 14/2007 on Biomedical Research, only research involving clinical interventions or the use of medical devices is required to undergo ethics committee review. Furthermore, Regulation (EU) 2016/679 (GDPR) and Organic Law 3/2018 establish that fully anonymized data are exempt from data protection requirements related to identifiable personal data. Therefore, considering that this study posed minimal risk to participants and complied with all relevant ethical and legal standards, formal review and approval by an ethics committee were not required. Participation was voluntary, and informed consent was obtained from all participants prior to data collection. The Centro Universitario Salus Infirmorum de Cádiz has approved the waiver of the Institutional Review Board Statement for this research.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.

Acknowledgments

We deeply appreciate all participants and experts for their invaluable contribution to this study.

Conflicts of Interest

The authors declare no conflicts of interest.

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Table 1. Descriptive comparison of personality traits across groups.
Table 1. Descriptive comparison of personality traits across groups.
VariableEXTAGRCONNEUROPEN
Total9.0 (7.0–11.0)12.0 (10.0–14.0)10.0 (8.0–12.0)8.0 (6.0–9.0)9.0 (7.0–11.0)
Gender
 Male10.0 (8.0–12.0)11.0 (9.0–13.0)9.0 (7.0–11.0)7.0 (5.0–9.0)10.0 (8.0–12.0)
 Female9.0 (7.0–11.0)12.0 (10.0–14.0)10.0 (8.0–13.0)8.0 (6.0–10.0)9.0 (7.0–11.0)
 p-value0.009 **0.000 **0.000 **0.000 **0.007 **
Sexual orientation
 Gay or lesbian8.5 (8.0–11.0)11.5 (9.0–14.0)8.0 (7.0–11.0) a8.5 (7.0–9.8)8.0 (7.0–10.0) a
 Heterosexual9.0 (7.0–11.0)12.0 (10.0–14.0)10.0 (8.0–12.0) b8.0 (6.0–9.0)9.0 (7.0–11.0) a
 Bisexual9.0 (7.0–11.8)12.0 (10.0–14.0)9.5 (8.0–11.0) a,b7.0 (4.2–9.0)10.0 (8.2–12.0) b
 p-value0.8550.7400.010 *0.1290.001 **
Religion
 Religious9.0 (7.0–11.0)12.0 (10.0–14.0)10.0 (8.0–13.0)8.0 (6.0–9.0)9.0 (7.0–11.0)
 Non-religious9.0 (7.0–11.0)11.0 (9.0–14.0)9.0 (7.0–11.0)8.0 (5.0–10.0)10.0 (8.0–11.0)
 p-value0.4710.001 **0.000 **0.4960.026 *
Having a partner
 Yes9.0 (7.0–11.0)12.0 (10.0–14.0)10.0 (8.0–13.0)8.0 (5.8–9.0)9.0 (7.0–11.0)
 No9.0 (7.0–12.0)12.0 (9.0–14.0)9.0 (7.0–12.0)8.0 (6.0–9.0)9.0 (8.0–11.0)
 p-value0.0800.004 **0.000 **0.9670.381
Type of relationship
 Open relationship8.0 (7.0–10.0)9.0 (8.0–12.0)8.0 (7.5–10.0)8.0 (6.8–10.0)8.0 (8.0–9.0)
 Exclusive or traditional9.0 (7.0–11.0)12.0 (10.0–14.0)10.0 (8.0–13.0)8.0 (5.0–9.0)9.0 (7.0–11.0)
 p-value0.5220.000 **0.009 *0.1740.521
Cohabitation as a couple
 Yes8.0 (7.0–11.0)12.0 (9.0–14.0)10.0 (8.0–12.0)8.0 (6.0–10.0)8.0 (6.0–10.0)
 No9.0 (7.0–11.0)12.0 (10.0–14.0)10.0 (8.0–12.0)8.0 (6.0–9.0)9.0 (7.0–11.0)
 p-value0.5690.9780.8570.1220.000 **
Use of contraception
 Yes9.0 (7.0–11.0)12.0 (10.0–14.0)10.0 (8.0–12.0)8.0 (5.0–9.0)9.0 (7.0–11.0)
 No9.0 (7.0–11.0)12.0 (9.0–14.0)9.0 (7.0–12.0)8.0 (6.0–10.0)8.0 (7.0–10.0)
 p-value0.3010.5350.6200.008 **0.013 *
Sexually transmitted infections
 Yes9.0 (7.8–10.2)11.0 (8.0–13.2)8.5 (6.8–10.2)8.0 (5.5–9.2)9.0 (7.0–10.5)
 No9.0 (7.0–11.0)12.0 (10.0–14.0)10.0 (8.0–12.0)8.0 (6.0–9.0)9.0 (7.0–11.0)
 p-value0.8680.0610.019 *0.730.890
Use of dating apps
 Yes10.0 (8.0–12.0)12.0 (9.0–14.0)9.0 (7.0–11.0)8.0 (6.0–9.0)9.0 (7.0–11.0)
 No9.0 (7.0–11.0)12.0 (10.0–14.0)10.0 (8.0–13.0)8.0 (5.0–9.0)9.0 (7.0–11.0)
 p-value0.020 *0.1940.000 **0.2310.526
Pornography consumption
 Yes10.0 (8.0–12.0)12.0 (9.0–14.0)9.0 (7.0–12.0)8.0 (5.0–9.0)10.0 (8.0–11.0)
 No9.0 (7.0–11.0)12.0 (10.0–14.0)10.0 (8.0–13.0)8.0 (6.0–9.0)9.0 (7.0–11.0)
 p-value0.000 **0.0520.000 **0.5680.002 **
Drug consumption
 Yes10.0 (8.0–12.0)12.0 (9.0–14.0)10.0 (8.0–12.0)8.0 (6.0–9.0)9.0 (7.0–11.0)
 No8.0 (6.0–10.0)12.0 (10.0–14.0)11.0 (8.0–13.0)8.0 (6.0–9.0)9.0 (7.0–10.5)
 p-value0.000 **0.8580.000 **0.5810.007 **
Drug consumption before or during sexual intercourse
 Yes8.0 (7.0–9.0)9.0 (8.0–10.0)8.0 (7.0–10.0)8.0 (6.0–8.0)9.0 (8.0–10.0)
 No9.0 (7.0–11.0)12.0 (10.0–14.0)10.0 (8.0–12.0)8.0 (6.0–9.0)9.0 (7.0–11.0)
 p-value0.3260.000 **0.039 *0.6700.906
EXT = extraversion, AGR = agreeableness, CON = conscientiousness, NEUR = neuroticism, OPEN = openness/intellect, * indicates a significant difference at p ≤ 0.05. ** indicates a significant difference at p ≤ 0.01. Groups with the same superscript letters showed no statistical difference after Dunn–Bonferroni post hoc test.
Table 2. Association between sexual attitudes (BRIEF), sociodemographic and behavioral variables.
Table 2. Association between sexual attitudes (BRIEF), sociodemographic and behavioral variables.
VariablePERMBCCOMMINST
Total2.9 (2.3–3.3)3.5 (3.0–4.0)3.6 (3.0–4.2)2.6 (2.0–3.0)
Gender
 Male3.0 (2.4–3.5)3.5 (3.0–4.0)3.6 (3.0–4.2)2.6 (2.0–3.0)
 Female2.8 (2.3–3.2)3.5 (3.0–4.5)3.6 (3.0–4.2)2.6 (2.0–3.0)
 p-value0.001 **0.7620.7630.202
Sexual orientation
 Gay or lesbian3.0 (2.3–3.2) a,b3.0 (2.5–4.0)3.4 (2.8–3.6)2.6 (1.9–3.2)
 Heterosexual2.8 (2.3–3.2) a3.5 (3.0–4.4)3.6 (3.0–4.2)2.6 (2.0–3.0)
 Bisexual3.1 (2.5–3.5) b3.5 (3.0–4.5)3.6 (3.2–4.2)2.5 (2.0–3.0)
 p-value0.015 *0.3290.1060.996
Religion
 Religious2.8 (2.2–3.1)3.5 (3.0–4.0)3.6 (3.0–4.2)2.8 (2.0–3.0)
 Non-religious3.1 (2.6–3.6)4.0 (3.0–4.5)3.6 (3.0–4.2)2.4 (2.0–3.0)
 p-value0.000 **0.0550.3270.057
Having a partner
 Yes2.7 (2.2–3.2)3.5 (3.0–4.0)3.6 (3.0–4.2)2.6 (2.0–3.0)
 No3.0 (2.5–3.4)3.5 (3.0–4.5)3.4 (3.0–4.0)2.8 (2.2–3.2)
 p-value0.000 **0.9070.0920.010 *
Type of relationship
 Open relationship3.4 (3.0–4.2)3.8 (3.0–4.6)4.1 (3.0–4.4)3.0 (1.9–3.8)
 Exclusive or traditional2.7 (2.2–3.2)3.5 (3.0–4.0)3.6 (3.0–4.2)2.6 (2.0–3.0)
 p-value0.000 **0.5070.2180.070
Cohabitation as a couple
 Yes2.8 (2.1–3.3)3.5 (3.0–4.0)3.6 (3.0–4.0)2.8 (2.2–3.2)
 No2.9 (2.3–3.3)3.5 (3.0–4.5)3.6 (3.0–4.2)2.6 (2.0–3.0)
 p-value0.4060.4700.4370.769
Use of contraception
 Yes2.9 (2.3–3.3)3.5 (3.0–4.0)3.6 (3.0–4.2)2.6 (2.0–3.0)
 No2.8 (2.2–3.2)3.5 (2.5–4.5)3.6 (3.0–4.0)2.6 (2.0–3.0)
 p-value0.0820.4100.6260.348
Sexually transmitted infections
 Yes3.1 (2.9–3.6)4.0 (3.0–5.0)3.5 (3.0–4.4)3.0 (2.2–3.2)
 No2.9 (2.3–3.2)3.5 (3.0–4.0)3.6 (3.0–4.2)2.6 (2.0–3.0)
 p-value0.009 **0.025 *0.9530.124
Use of dating apps
 Yes3.1 (2.7–3.6)3.5 (3.0–4.5)3.6 (3.0–4.3)2.8 (2.0–3.2)
 No2.8 (2.2–3.2)3.5 (3.0–4.0)3.6 (3.0–4.2)2.6 (2.0–3.0)
 p-value0.000 **0.9320.042 *0.122
Pornography consumption
 Yes3.1 (2.5–3.5)3.5 (3.0–4.5)3.6 (3.0–4.2)2.6 (2.0–3.0)
 No2.7 (2.2–3.1)3.5 (3.0–4.0)3.6 (3.0–4.2)2.6 (2.0–3.0)
 p-value0.000 **0.3590.3760.589
Drug consumption
 Yes3.0 (2.5–3.4)3.5 (3.0–4.5)3.6 (3.0–4.2)2.8 (2.2–3.2)
 No2.7 (2.1–3.1)3.0 (3.0–4.0)3.4 (3.0–4.0)2.6 (2.0–3.0)
 p-value0.000 **0.0820.0710.004 *
Drug consumption before or during sexual intercourse
 Yes3.2 (3.0–4.2)3.0 (3.0–4.0)3.8 (3.0–4.2)3.2 (3.0–3.6)
 No2.8 (2.3–3.2)3.5 (3.0–4.5)3.6 (3.0–4.2)2.6 (2.0–3.0)
 p-value0.000 **0.4430.6270.000 **
PERM = permissiveness, BC = birth control, COMM = communion, INST = instrumentality, * indicates a significant difference at p ≤ 0.05. ** indicates a significant difference at p ≤ 0.01. Groups with the same superscript letters showed no statistical difference after Dunn–Bonferroni post hoc test. Figures in bold indicate the group with significantly higher values.
Table 3. Spearman rank correlations between age, personality traits and sexual attitudes.
Table 3. Spearman rank correlations between age, personality traits and sexual attitudes.
AgeEXTAGRRESNEUROPENPERMBCCOMMINST
Age1
EXT−0.0051
AGR−0.0010.221 **1
CON0.106 **−0.0050.212 **1
NEUR0.008−0.158 **−0.089 *−0.190 *1
OPEN−0.074 *0.272 **0.181 **0.078 *−0.290 **1
PERM0.0230.106 **−0.114 **−0.178 **−0.0180.0701
BC−0.0240.0700.085 *0.048−0.076 *0.0460.161 **1
COMM0.0460.180 **0.169 **0.052−0.0660.0730.185 **0.286 **1
INST−0.009−0.019−0.181 **−0.086 *0.027−0.0620.375 **0.082 *0.160 **1
EXT = extraversion, AGR = agreeableness, CON = conscientiousness, NEUR = neuroticism, OPEN = openness/intellect, PERM = permissiveness, BC = birth control, COMM = communion, INST = instrumentality. * indicates a significant correlation at p ≤ 0.05. ** indicates a significant correlation at p ≤ 0.01.
Table 4. Stepwise logistic regression of factors influencing condom use, drug consumption, pornography consumption and dating app use among health science students.
Table 4. Stepwise logistic regression of factors influencing condom use, drug consumption, pornography consumption and dating app use among health science students.
CONDOM USE
Independent VariableCoefficient (β)S.E.Waldp-ValueOR95% CI for ORInverse OR
Age−0.1040.02715.2750.0000.9010.855–0.9491.110
Gender a−1.2740.22731.4340.0000.2800.179–0.4373.571
Sexual orientation 6.9000.032
 HeterosexualRef
 Gay or lesbian−1.0600.4226.3010.0120.3460.151–0.7932.890
 Bisexual−0.2620.2910.8110.3680.7700.435–1.361
Type of relationship 8.1540.017
 Exclusive/traditionalRef
 No relationship0.4750.1717.7200.0051.6071.150–2.246
 Open relationship−0.1710.5780.0870.7680.8430.272–2.617
Pornography consumption b−0.4830.2005.8560.0160.6170.417–0.9121.621
Openness/intellect0.0860.0307.9140.0051.0901.026–1.157
Constant2.6300.71913.3870.00013.873
DRUG CONSUMPTION
Age−0.0950.01540.2070.0000.9100.884–0.9371.100
Pornography consumption b0.9780.18428.1290.0002.6591.853–3.817
Permissiveness0.4400.10318.0800.0001.5521.267–1.901
Extraversion0.1000.02614.6030.0001.1051.050–1.163
PORNOGRAPHY CONSUMPTION
Gender a−2.2290.198126.4700.0000.1080.073–0.1599.260
Drugs consumption c0.8830.20418.7550.0002.4191.622–3.607
Use of dating apps d1.3280.23531.9520.0003.7742.381–5.982
Permissiveness0.3910.11810.8760.0011.4781.172–1.864
Instrumentality−0.3560.1208.8050.0030.7000.554–0.8861.429
DATING APP USE
Sexual orientation 37.4840.000
 HeterosexualRef
 Gay or lesbian2.5090.45031.0900.00012.2965.090–29.705
 Bisexual1.0030.3239.6750.0022.7271.449–5.131
Type of relationship 38.4130.000
 Exclusive/traditionalRef
 No relationship1.4120.25431.0240.0004.1042.497–6.745
 Open relationship2.5520.64515.6310.00012.8323.621–45.469
Cohabitation as a couple e0.9710.3876.2930.0122.6411.237–5.641
Pornography consumption b1.2540.22531.1660.0003.5052.257–5.444
Religion f−0.6380.2089.3980.0020.5280.351–0.7941.894
Communion0.3990.1418.0490.0051.4911.131–1.964
Constant−4.1730.61046.8470.0000.015
Note: for each dependent variable only significant coefficients are shown. N = 730; S.E.: standard error of coefficient; OR: odds ratio; CI: confidence interval. a 0 = male, 1 = female; b,c,d,e 0 = no, 1 = yes. f 0 = non-religious, 1 = religious.
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Naranjo-Márquez, M.; Bocchino, A.; Gilart, E.; Cotobal-Calvo, E.M.; Mata-Pérez, C.; Palazón-Fernández, J.L. Personality Traits and Sexual Attitudes as Predictors of Risky Sexual Behaviors in Health Science Students. Youth 2026, 6, 19. https://doi.org/10.3390/youth6010019

AMA Style

Naranjo-Márquez M, Bocchino A, Gilart E, Cotobal-Calvo EM, Mata-Pérez C, Palazón-Fernández JL. Personality Traits and Sexual Attitudes as Predictors of Risky Sexual Behaviors in Health Science Students. Youth. 2026; 6(1):19. https://doi.org/10.3390/youth6010019

Chicago/Turabian Style

Naranjo-Márquez, María, Anna Bocchino, Ester Gilart, Eva Manuela Cotobal-Calvo, Concepción Mata-Pérez, and José Luis Palazón-Fernández. 2026. "Personality Traits and Sexual Attitudes as Predictors of Risky Sexual Behaviors in Health Science Students" Youth 6, no. 1: 19. https://doi.org/10.3390/youth6010019

APA Style

Naranjo-Márquez, M., Bocchino, A., Gilart, E., Cotobal-Calvo, E. M., Mata-Pérez, C., & Palazón-Fernández, J. L. (2026). Personality Traits and Sexual Attitudes as Predictors of Risky Sexual Behaviors in Health Science Students. Youth, 6(1), 19. https://doi.org/10.3390/youth6010019

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