Addressing Police Occupational Safety During an Opioid Crisis
Bibliographic record
Abstract
OBJECTIVE: To develop and validate syringe threat and injury correlates (STIC) score to measure police vulnerability to needlestick injury (NSI). METHODS: Tijuana police officers (N = 1788) received NSI training (2015 to 2016). STIC score incorporates five self-reported behaviors: syringe confiscation, transportation, breaking, discarding, and arrest for syringe possession. Multivariable logistic regression was used to evaluate the association between STIC score and recent NSI. RESULTS: Twenty-three (1.5%) officers reported NSI; higher among women than men (3.8% vs 1.2%; P = 0.007). STIC variables had high internal consistency, a distribution of 4.0, a mode of 1.0, a mean (sd) of 2.0 (0.8), and a median (interquartile range [IQR]) of 2.0 (1.2 to 2.6). STIC was associated with recent NSI; odds of NSI being 2.4 times higher for each point increase (P-value <0.0001). CONCLUSIONS: STIC score is a novel tool for assessing NSI risk and prevention program success among police.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".