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Equity as the fourth ‘E’ in the ‘3 E’s’ approach to injury prevention

2019· article· en· W2973728879 on OpenAlexaff
Audrey R. Giles, Michelle E. E. Bauer, Janet Jull

Bibliographic record

VenueInjury Prevention · 2019
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsQueen's UniversityUniversity of Ottawa
Fundersnot available
KeywordsEnforcementEquity (law)Injury preventionSuicide preventionPoison controlOccupational safety and healthHuman factors and ergonomicsRisk preventionPrevention scienceEngineeringBusinessPublic relationsMedicinePolitical scienceRisk analysis (engineering)Medical emergencyPublic healthNursingLaw

Abstract

fetched live from OpenAlex

The education, engineering and enforcement (3 E's) approach to injury prevention is grounded in assumptions that it is effective for everyone; however, evidence demonstrates that it fails to consider opportunities for all populations to experience safe and injury-free lives. In this way, the 3 E's approach does not support health equity in the injury prevention field. In this brief report, we argue that a fourth E, equity, must also be used with the 3 E's approach to injury prevention.

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)0.006
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.555
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.047
GPT teacher head0.388
Teacher spread0.341 · 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 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

Citations14
Published2019
Admission routes1
Has abstractyes

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