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Record W7029096373

Influence des facteurs socioenvironnementaux sur la prévalence de la violence dans les relations amoureuses chez les adolescent-e-s à Montréal

2023· dissertation· fr· W7029096373 on OpenAlexaboutno aff

Bibliographic record

VenueArchipelago (Université du Québec à Montréal) · 2023
Typedissertation
Languagefr
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsnot available
Fundersnot available
KeywordsPoison controlPopulationGender relationsFamily life
DOInot available

Abstract

fetched live from OpenAlex

Dating violence (DV) is a widespread phenomenon among adolescents and is likely to have significant negative consequences for victims’ health and well-being. Although individual, family, and peer determinants of DV have been widely studied, knowledge regarding the influence of socio-environmental characteristics of residential neighbourhoods on DV remains limited. This doctoral thesis aims to assess the association between neighbourhood characteristics and both experienced and perpetrated DV among adolescents living on the island of Montreal. It focuses more specifically on the effects of certain sociodemographic characteristics (socioeconomic status (SES), single-parent families, residential instability, and ethnocultural diversity), several built-environment features (density of alcohol outlets, density of bars, density of community organizations, density of parks, greenness, and walkability), and social-environment characteristics (crime, social support, and social participation) on DV. The thesis also seeks to explore the modifying effect of gender and the influence of spatial scale in the analysis of these relationships.To address these objectives, data from the Québec Health Survey of High School Students (QHSHSS) were used to measure DV as well as neighbourhood social support and social participation. Egocentric neighbourhoods were operationalized for all participants with a postal code on the island of Montreal using polygon-based network buffers of four different sizes (250 m, 500 m, 750 m, and 1,000 m). Population census data (2016) were used to describe neighbourhood sociodemographic characteristics. Various data sources were used to measure built-environment features and crime. Associations between socio-environmental factors and both experienced and perpetrated DV were estimated using logistic regression models. All analyses were conducted separately for girls and boys to assess gender-specific effects.The results are presented in three scientific articles. The first article, entitled “Assessing the influence of spatial scale on the effects of neighborhood sociodemographic characteristics on dating violence” and submitted to Social Science Research, describes analyses of the relationships between sociodemographic characteristics and DV. The second article, “Associations between neighborhood characteristics and dating violence: does spatial scale matter?”, published in the International Journal of Health Geographics, examines the associations between built-environment features and crime, on the one hand, and DV on the other. Finally, the third article, “Neighborhood social support and social participation as predictors of dating violence”, submitted to the Journal of Interpersonal Violence, investigates the association between social participation and social support in the community environment, on the one hand, and DV on the other.Findings from these studies suggest that several neighbourhood characteristics are associated with DV. The effects of these factors vary according to gender, the specific form of DV considered, and the spatial scale of analysis. Among girls, SES, residential instability, bar density, and walkability are associated with psychological DV victimization, while single-parenthood, ethnocultural diversity, and walkability are associated with physical/sexual DV victimization. Social support is associated with the perpetration of psychological DV, whereas social participation is associated with the perpetration of physical/sexual DV. Among boys, single-parenthood and greenness are linked to psychological DV victimization, while crime is associated with both victimization and perpetration of physical/sexual DV. Residential instability, ethnocultural diversity, alcohol outlet density, and social participation are associated with the perpetration of psychological DV.Furthermore, the results suggest that the effects of SES, single-parenthood, ethnocultural diversity, density of community organizations, and crime are primarily observable at finer spatial scales (250 m or 500 m), whereas the effects of residential instability, alcohol outlet density, walkability, and greenness tend to emerge at larger scales (500 m to 1,000 m). Neighbourhoods therefore appear to play an important role in DV, and several socio-environmental factors may influence these behaviors. The findings of this thesis also highlight the importance of considering gender, the specific form of DV, and the choice of spatial scale to achieve a better understanding of these relationships. Moreover, these results have significant implications for practice, as they point to new avenues for intervention development, particularly emphasizing the importance of improving neighbourhood conditions (e.g., programs enhancing social cohesion, greening initiatives, urban design improvements) to reduce the prevalence of DV.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.209
Threshold uncertainty score0.421

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.229
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

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