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Record W4386485089 · doi:10.3390/socsci12090501

“Stepping Up”: A Decade of Relationship Violence Prevention

2023· article· en· W4386485089 on OpenAlexafffundabout
Catherine Carter‐Snell, D. Gaye Warthe

Bibliographic record

VenueSocial Sciences · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsMount Royal University
FundersGovernment of Alberta
KeywordsPsychologySuicide preventionOccupational safety and healthPoison controlInjury preventionMental healthSexual violenceProgram evaluationMedical educationClinical psychologyApplied psychologyEnvironmental healthMedicinePsychiatryCriminologyPolitical science

Abstract

fetched live from OpenAlex

Students in postsecondary education are at high risk for experiencing relationship violence, including dating, domestic, and sexual violence. This can result in significant mental and physical health consequences. A relationship violence prevention program has been offered and evaluated for over 10 years at a Canadian university. It is based on a social–ecological model of violence prevention and best practices. Students who completed both pre- and post-program evaluations were used as their own controls to evaluate the effects of the program. Significant changes were noted for most aspects of the program in knowledge, attitudes, and behavioural intents each year, and these changes persisted for up to six months on most measures. The sample sizes were small and potentially overestimated the effect of the program if results were reported individually. Meta-analysis was used to pool the data and examine the effects of the program across the decade. The results indicated that the program was effective in changing knowledge, attitudes, and behavioural intents immediately following the program, but there were insufficient paired data to conduct six-month meta-analyses. Suggestions are made for future programs and further research.

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)0.002
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.555
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0020.001
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 head0.136
GPT teacher head0.436
Teacher spread0.300 · 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 designTheoretical or conceptual
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

Citations1
Published2023
Admission routes3
Has abstractyes

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