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Record W4306928221 · doi:10.3389/fpsyt.2022.933433

Exploring risk and protective factors for adolescent dating violence across the social-ecological model: A systematic scoping review of reviews

2022· article· en· W4306928221 on OpenAlexafffund
Caroline Claussen, Emily Matejko, Deinera Exner‐Cortens

Bibliographic record

VenueFrontiers in Psychiatry · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsUniversity of Calgary
FundersAlberta Children's Hospital Research InstituteSocial Sciences and Humanities Research Council of CanadaCanada Research ChairsUniversity of Calgary
KeywordsPsychologyMental healthAggressionClinical psychologyApplied psychologySocial psychologyPsychiatry

Abstract

fetched live from OpenAlex

Background: Adolescent dating violence (ADV) is a serious issue that affects millions of youth worldwide. ADV can be any intentional psychological, emotional, physical, or sexual aggression that occurs in adolescent dating and/or sexual relationships, and can occur both in person and electronically. The mental health consequences of ADV can be significant and far reaching, with studies finding long-term effects of dating violence victimization in adolescence. Preventing ADV so that youth do not experience negative mental health consequences is thus necessary. To be effective, however, prevention efforts must be comprehensive and address more than one domain of the social-ecological model, incorporating risk and protective factors across the individual level; relationship level; community level; and societal level. To support researchers and practitioners in designing such prevention programs, an understanding of what risk and protective factors have been identified over the past several decades of ADV research, and how these factors are distributed across levels of the social-ecological model, is needed. Methods: This study was conducted in accordance with PRISMA guidelines. We included peer-reviewed articles published in English between January 2000 and September 2020. The search strategy was developed in collaboration with a research librarian. Covidence was used for title and abstract screening and full text review. Data were extracted from included articles using a standardized charting template, and then synthesized into tables by type of factor (risk or protective), role in ADV (victimization or perpetration), and level(s) of the social-ecological model (individual, relationship, community, societal). Results: Our initial search across six databases identified 4,798 potentially relevant articles for title and abstract review. Following title and abstract screening and full text review, we found 20 articles that were relevant to our study objective and that met inclusion criteria. Across these 20 articles, there was a disproportionate focus on risk factors at the individual and relationship levels of the social-ecological model, particularly for ADV perpetration. Very little was found about risk factors at the community or societal levels for ADV victimization or perpetration. Furthermore, a very small proportion of articles identified any protective factors, regardless of level of the social-ecological model. Conclusion: Despite best practice suggesting that ADV prevention strategies should be comprehensive and directed at multiple levels of an individual's social ecology, this systematic scoping review of reviews revealed that very little is known about risk factors beyond the individual and relationship level of the social-ecological model. Further, past research appears steeped in a risk-focused paradigm, given the limited focus on protective factors. Research is needed that identifies risk factors beyond the individual and relationship levels, and a strengths-based focus should be used to identify novel protective factors. In addition, a more critical approach to ADV research - to identify structural and not just individual risk and protective factors - is needed.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.645
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.109
GPT teacher head0.385
Teacher spread0.276 · 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 teacher head, not a consensus.

Study designSystematic review
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

Citations35
Published2022
Admission routes2
Has abstractyes

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