Search Results (8)

Introduction: Child trafficking is a clear violation of human rights, robbing minors of their fundamental entitlements. These encompass the right to personal identity, familial bonds, cultural heritage, access to healthcare and proper nourishment, education, freedom of speech, and the assurance of safety and security. Children and young people, given their inherent vulnerability and limited access to support networks, frequently struggle to safeguard themselves effectively. This predicament presents traffickers with opportunities to exploit and manipulate them. Therefore, it is essential for professionals across various sectors—including education, healthcare, protective and social services, as well as the justice system—to undergo comprehensive training and be integrated into a robust social protection system. This preparation should equip them to conduct screenings, accurately assess needs, and adhere to international guidelines when addressing cases of child trafficking. Aim: The aim of this study is to explore the child-centered anti-trafficking approaches employed by Italy and Turkey, situated along migration pathways in the Mediterranean region and experiencing significant regular and irregular migration flows in recent years. Both nations fall within the classification of southern European welfare regimes. Methodology: This research specifically delves into the social protection policies aimed at children and young victims established by these two countries. Carried out between 1 February 2020 and 20 May 2021, this study employed a semi-structured interview approach, conducting qualitative in-depth interviews in both Italy and Turkey. This research targeted experts from various disciplines engaged in combating human trafficking in both countries, with a total sample size of 46 participants, comprising 15 experts from Italy and 31 from Turkey. Grounded theory formed the basis of the study, with data analyzed using the MAXQDA 2020 Pro Analytics program, employing a multidisciplinary and empowerment approach. Results: The analysis yielded 2942 codes, 17 sub-themes, and four main themes. The study identified four main themes: (i) characteristics of child victims and vulnerable child groups, (ii) services provided to at-risk groups and child trafficking victims within the current national counter-trafficking framework, (iii) challenges encountered in delivering services to children and young individuals, and (iv) recommendations for establishing an effective and child-centered protection system. Discussion and Conclusion: It is imperative to ensure that victims of child trafficking have access to comprehensive social protection measures. It has been noted that both Italy and Turkey offer various services to victims of child trafficking, including in-kind and -cash social assistance, free legal aid, shelter services, access to education and healthcare, as well as prevention, awareness, and advocacy programs. However, there are also differences between the two countries in certain aspects. Recommendations aimed at addressing these differences can be developed by adhering to the minimum standards outlined in the Council of Europe Convention on Action against Trafficking in Human Beings. Full article

This study investigates the impact of language contact on three generations of bilingual Spanish and Uruguayan Portuguese speakers in Rivera City, Uruguay, located on the Uruguayan–Brazilian border. Focusing on the confirmed presence of the Portuguese-like/b/and/v/phonemic distinction, and the lower frequency of the Montevideo Spanish-like approximantized stops in Riverense Spanish (RS), the research examines the production of <v> and <b> in 29 female Rivera Spanish bilinguals belonging to different age groups. More specifically, the aim was to see if the previously observed differential use of language-specific phonological variants could be accounted for by using precise measurements of relative intensity, duration, and voicing coupled with a distributional analysis of realizations derived from auditory coding. At the same time, their production is compared to that of 30 monolingual Montevideo Spanish (MS) speakers, who served as the control group, offering a first description of the production of <v> and <b> within this distinct Rioplatense Spanish variety. Riverense’s higher overall relative intensity, duration, and voicing values support auditory coding results, providing evidence of the expected phonological differences between both Uruguayan Spanish varieties. In particular, an exclusive presence of fricative/v/and less approximantization of/b/in RS speech exposed the influence of Portuguese in Rivera bilinguals and their divergence from MS. In addition, as predicted, the findings reveal a higher presence of Portuguese-like productions of [v] and [b] in older bilinguals when compared to younger generations. This illustrates a continuum from Portuguese-like forms to Spanish-like forms, which is confirmed by both acoustic and distributional analyses. Finally, evidence of the existence of innovative forms resulting from mixing Portuguese and Spanish phonological systems in RS are presented. This study’s findings contribute to sociolinguistics and bilingualism by exposing cross-linguistic influence in a border setting with rigorous analytical methods that offer reliable results and go beyond a basic analysis based on auditory identification. Full article

This paper presents a novel analytical approach for improving patients’ experience in healthcare settings. The analytical tool uses a classifier and a recommend management approach to facilitate decision making in a timely manner. The designed methodology comprises of 4 key stages, which include developing a bot to scrap web data while performing sentiment analysis and extracting keywords from National Health Service (NHS) rate and review webpages, building a classifier with Waikato Environment for Knowledge Analysis (WEKA), analyzing speech with Python, and using Microsoft Excel for analysis. In the selected context, a total of 178 reviews were extracted from General Practitioners (GP) websites within Northamptonshire County, UK. Accordingly, 4764 keywords such as “kind”, “exactly”, “discharged”, “long waits”, “impolite staff”, “worse”, “problem”, “happy”, “late” and “excellent” were selected. In addition, 178 reviews were analyzed to highlight trends and patterns. The classifier model grouped GPs into gold, silver, and bronze categories. The outlined analytical approach complements the current patient feedback analysis approaches by GPs. This paper solely relied upon the feedback available on the NHS’ rate and review webpages. The contribution of the paper is to highlight the integration of easily available tools to perform higher level of analysis that provides understanding about patients’ experience. The context and tools used in this study for ranking services within the healthcare domain is novel in nature, since it involves extracting useful insights from the provided feedback. Full article

Current evidence suggests that attitude towards older people may be associated with sociodemographic and educational variables; hence, a positive attitude towards older people is key when training new university professionals. However, there is little evidence of this association in Chilean university students. The objective was to analyze students from a Chilean university’s attitudes towards older people, according to sociodemographic and educational variables. Analytical and cross-sectional study; 515 students from a Chilean university were consulted online about their attitude towards older people using Kogan’s Attitudes towards Old People scale. Additionally, sociodemographic and educational variables were recorded. The average score for positive attitude was 70.8 (±9.7), while the negative attitude score was 68.3 (±11.6). The total score was 139.1 (±16.6). Mostly, university students perceive themselves with a low-level positive attitude (61.2%). Additionally, older university students (26–42 years old); women; Chileans; students of law, speech therapy, and occupational therapy; students in their final years of the programs; and those who had training in older people outside the university have a more positive attitude towards older people. In Conclusion, a profile of sociodemographic and educational characteristics of students with a lower and higher attitude towards older age was investigated. These results are relevant since the way of seeing the aging process could regulate the training of future professionals and consequently generate changes in dealing with older people. Young people’s perception of ageing would affect the treatment and incorporation of the older people in society and the adaptation of policies in this age group. Full article

One of the big challenges in treating individuals with bipolar disorder (BD) is nonadherence to medication. This is the principal factor associated with a worse prognosis or outcome of the disease. This study aimed to explore and analyze the individual perceptions that people with BD have about the positive and negative aspects when taking medication. A descriptive and interpretative study was carried out using the qualitative research paradigm with the use of the analytical technique of discourse analysis, extracting the data through the completion of focus groups. Participants’ speech was digitally audio-recorded in digital format. In order to complete the codification of the participants’ speech content, we relied on the qualitative data analysis (using the QRS NVivo 10 computer software). Thirty-six participants diagnosed with bipolar disorder took part in our study. In the participants’ speech concerning the main barriers to pharmacological treatment, three key topics were identified. Perceived facilitators were summarized in four factors. The main facilitators regarding the use of pharmacological treatment in individuals with BD were the ones related with the perceived need for treatment in the acute phase, the recognition of the illness, the shared clinical decision, and the causal biological attribution in the chronic phase. In terms of perceived barriers, social control was identified in both phases, adverse effects in the acute phase, and the absence of effective treatment in the chronic state. Full article

Introduction: Recently, the tendency of artificial intelligence (AI) and big data use/applications has been rapidly expanding across the globe, improving people’s lifestyles with data-driven services (i.e., recommendations, smart healthcare, etc.). The synergy between AI and big data has become imperative considering the drastic growth in personal data stemming from diverse sources (cloud computing, IoT, social networks, etc.). However, when data meet AI at some central place, it invites unimaginable privacy issues, and one of those issues is group privacy. Despite being the most significant problem, group privacy has not yet received the attention of the research community it is due. Problem Statement: We study how to preserve the privacy of particular groups (a community of people with some common attributes/properties) rather than an individual in personal data handling (i.e., sharing, aggregating, and/or performing analytics, etc.), especially when we talk about groups purposely made by two or more people (with clear group identifying markers), for whom we need to protect their privacy as a group. Aims/Objectives: With this technical letter, our aim is to introduce a new dimension of privacy (e.g., group privacy) from technical perspectives to the research community. The main objective is to advocate the possibility of group privacy breaches when big data meet AI in real-world scenarios. Methodology: We set a hypothesis that group privacy (extracting group-level information) is a genuine problem, and can likely occur when AI-based techniques meet high dimensional and large-scale datasets. To prove our hypothesis, we conducted a substantial number of experiments on two real-world benchmark datasets using AI techniques. Based on the experimental analysis, we found that the likelihood of privacy breaches occurring at the group level by using AI techniques is very high when data are sufficiently large. Apart from that, we tested the parameter effect of AI techniques and found that some parameters’ combinations can help to extract more and fine-grained data about groups. Findings: Based on experimental analysis, we found that vulnerability of group privacy can likely increase with the data size and capacity of the AI method. We found that some attributes of people can act as catalysts in compromising group privacy. We suggest that group privacy should also be given due attention as individual privacy is, and robust tools are imperative to restrict implications (i.e., biased decision making, denial of accommodation, hate speech, etc.) of group privacy. Significance of results: The obtained results are the first step towards responsible data science, and can pave the way to understanding the phenomenon of group privacy. Furthermore, the results contribute towards the protection of motives/goals/practices of minor communities in any society. Concluding statement: Due to the significant rise in digitation, privacy issues are mutating themselves. Hence, it is vital to quickly pinpoint emerging privacy threats and suggest practical remedies for them in order to mitigate their consequences on human beings. Full article

This paper reviews 74 empirical publications that used high-frequency data collection tools to capture facets of small collaborative groups—i.e., papers that conduct Multimodal Collaboration Analytics (MMCA) research. We selected papers published from 2010 to 2020 and extracted their key contributions. For the scope of this paper, we focus on: (1) the sensor-based metrics computed from multimodal data sources (e.g., speech, gaze, face, body, physiological, log data); (2) outcome measures, or operationalizations of collaborative constructs (e.g., group performance, conditions for effective collaboration); (3) the connections found by researchers between sensor-based metrics and outcomes; and (4) how theory was used to inform these connections. An added contribution is an interactive online visualization where researchers can explore collaborative sensor-based metrics, collaborative constructs, and how the two are connected. Based on our review, we highlight gaps in the literature and discuss opportunities for the field of MMCA, concluding with future work for this project. Full article

Collaboration is an important 21st Century skill. Co-located (or face-to-face) collaboration (CC) analytics gained momentum with the advent of sensor technology. Most of these works have used the audio modality to detect the quality of CC. The CC quality can be detected from simple indicators of collaboration such as total speaking time or complex indicators like synchrony in the rise and fall of the average pitch. Most studies in the past focused on “how group members talk” (i.e., spectral, temporal features of audio like pitch) and not “what they talk”. The “what” of the conversations is more overt contrary to the “how” of the conversations. Very few studies studied “what” group members talk about, and these studies were lab based showing a representative overview of specific words as topic clusters instead of analysing the richness of the content of the conversations by understanding the linkage between these words. To overcome this, we made a starting step in this technical paper based on field trials to prototype a tool to move towards automatic collaboration analytics. We designed a technical setup to collect, process and visualize audio data automatically. The data collection took place while a board game was played among the university staff with pre-assigned roles to create awareness of the connection between learning analytics and learning design. We not only did a word-level analysis of the conversations, but also analysed the richness of these conversations by visualizing the strength of the linkage between these words and phrases interactively. In this visualization, we used a network graph to visualize turn taking exchange between different roles along with the word-level and phrase-level analysis. We also used centrality measures to understand the network graph further based on how much words have hold over the network of words and how influential are certain words. Finally, we found that this approach had certain limitations in terms of automation in speaker diarization (i.e., who spoke when) and text data pre-processing. Therefore, we concluded that even though the technical setup was partially automated, it is a way forward to understand the richness of the conversations between different roles and makes a significant step towards automatic collaboration analytics. Full article