MétaCan
Menu
Back to cohort
Record W2022181730 · doi:10.5539/gjhs.v4n2p77

Educating Youths to Make Safer Choices: Results of a Program Evaluation Study

2012· article· en· W2022181730 on OpenAlexaffvenueabout
Donna M. Wilson, Carrie Chamberland, Jessica A. Hewitt

Bibliographic record

VenueGlobal Journal of Health Science · 2012
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsMisericordia Community HospitalUniversity of Alberta
Fundersnot available
KeywordsSAFERInjury preventionSuicide preventionHuman factors and ergonomicsOccupational safety and healthPoison controlProgram evaluationPsychologyEducational programMedicineMedical educationGerontologyMedical emergencyComputer securityPolitical scienceComputer science

Abstract

fetched live from OpenAlex

Injuries are a leading cause of childhood death and disability. Many injuries are a result of youths taking risks and not avoiding risky situations. An educational program to reduce adolescent injury risk (Prevent Alcohol and Risk-Related Trauma in Youth) has operated out of the Misericordia Hospital in Edmonton Canada since 1992. This reality-based program was evaluated to see if it was impacting program participants. An increase in correct answers for some knowledge, behavior, and attitude questions were found at one week and one month following this 1-day reality-based program. This program was thus considered as having some relevancy in educating grade-9 youths. Although a longitudinal study is needed to determine if this relevancy is long term, this study highlights the importance of reality-based public health programs.

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)0.019
metaresearch head score (Gemma)0.019
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.102

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.019
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.002
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 head0.133
GPT teacher head0.522
Teacher spread0.389 · 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
Published2012
Admission routes3
Has abstractyes

Explore more

Same venueGlobal Journal of Health ScienceSame topicInjury Epidemiology and PreventionFrench-language works237,207