Comparing Practices Used in Overdose Fatality Review Teams to Recommended Implementation Guidelines
Bibliographic record
Abstract
OBJECTIVES: Overdose fatality review teams are a public health and public safety collaboration that reviews fatality cases using a multidisciplinary team to provide recommendations for overdose prevention. No research exists on the case review practices currently being used in these programs. DESIGN: We administered a cross-sectional survey measuring case review practices and perceptions to a convenience sample of overdose fatality review teams. SETTING: We administered the online survey to participants at a national virtual forum on overdose fatality review. PARTICIPANTS: In this study, we examined 30 county-level overdose fatality review teams from 6 states who completed the survey. MAIN OUTCOME MEASURES: We developed measures of case review practices from an overdose fatality review implementation guide. We provided descriptive statistics on the survey items used to measure these practices and examined how practice uptake varied by overdose fatality review team characteristics. RESULTS: Most overdose fatality review teams had adequate representation and membership, but none adhered to all of the practices measured from the implementation guide. The largest gap was in perceived effectiveness and implementation of case review recommendations. In addition, teams that had been reviewing cases for longer reported more adherence to recommended practices. CONCLUSIONS: Overdose fatality case review is a collaboration between local public health and public safety agencies that holds great promise. However, these teams will require additional training and technical assistance with local community support to ensure that recommendations are actionable.
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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 | 0.058 | 0.242 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| 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".