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Record W2041069359 · doi:10.1177/0829573515576122

Violence Exposure and Victimization Among Rural Adolescents

2015· article· en· W2041069359 on OpenAlexaffabout
David Mykota, Adele Laye

Bibliographic record

VenueCanadian Journal of School Psychology · 2015
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsPsychologyInjury preventionHuman factors and ergonomicsPoison controlSuicide preventionOccupational safety and healthEnvironmental healthRural areaMedicine

Abstract

fetched live from OpenAlex

Violence exposure is a serious public health concern for adolescents in schools today. Violence exposure can be quite severe and frequent with multiple acts of indirect and direct victimization having lasting effects on the physical, emotional, and intellectual well-being of adolescents. The purpose of the present study is to examine the rates of violence exposure and the relative risk for multiple exposures among adolescent youth living in rural communities. Results confirm that adolescents who live in rural areas were frequent victims of violence exposure and that males were more likely to be the victims than females. Moreover, the relative risk for multiple exposures either indirectly, directly, or in combination reveal that in all instances amplification of risk occurs. The study is an important first step in understanding the rates of violence exposure and victimization experienced by adolescent youth in rural Canada with implications for school-based programming presented.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.093
Threshold uncertainty score0.185

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.027
GPT teacher head0.295
Teacher spread0.268 · 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

Citations5
Published2015
Admission routes2
Has abstractyes

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