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Attitudes about Injury among High School Students

2008· article· en· W2094042687 on OpenAlexaff
Olivier Monneuse, Avery B. Nathens, Nicole N. Woods, Julie L. Mauceri, Sonya Canzian, Wei Xiong, Najma Ahmed

Bibliographic record

VenueJournal of the American College of Surgeons · 2008
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsThe Wilson CentreSt. Michael's HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineSAFERPerceptionInjury preventionSuicide preventionRisk perceptionHuman factors and ergonomicsQualitative researchPoison controlOccupational safety and healthYoung adultFamily medicineMedical educationGerontologyEnvironmental healthComputer securityPathologyPsychology

Abstract

fetched live from OpenAlex

BACKGROUND: Despite education and changes in public policy, trauma-related injuries continue to exact an unacceptably high morbidity and mortality, particularly among young people. Most injuries are preventable and can often be attributed to poor choices. STUDY DESIGN: A mixed methods study involving 262 high school students was conducted to study the effect on knowledge and risk assessment after a day-long injury prevention program, and to develop a theoretic framework to better understand attitudes and beliefs that underlie commonly seen behaviors among young people. RESULTS: Knowledge about injury increased after participation in the program, but was not durable over time. Risk perception and capacity to discern safer options improved after the program and persisted for up to 30 days. A qualitative analysis revealed seven themes that reflect a sense of invincibility and a belief that fate is more important than choice in determining the outcomes of a situation. CONCLUSIONS: Effective injury prevention programs should include risk perception training that is informed by the attitudes and beliefs of the recipients.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.029
Threshold uncertainty score0.314

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.017
GPT teacher head0.320
Teacher spread0.303 · 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.

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

Citations21
Published2008
Admission routes1
Has abstractyes

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