Understanding the social determinants of opioid related hospitalizations
Bibliographic record
Abstract
IntroductionThe current opioid crisis is recognized by governments at many levels as an urgent priority. While there is basic demographic information on who experiences opioid related adverse events, there is little information to provide a more fulsome profile of those experiencing these events for targeted intervention and forward projection estimation. Objectives and ApproachThis study is the first to use a nationally-representative Census of Population linked with health administrative data to examine opioid-related hospitalization patterns across income and Aboriginal status. ResultsPreliminary analyses using 2006 Census-hospital linked database found cohort rates of opioid-related hospitalizations are up to 7 times higher among Aboriginal youth, and also for young adults (12-19/ 20-24) compared with non-Aboriginal youth or young adults. Rates among these youth living on reserves were 8.4 times higher; among those off reserve 8.7 times higher. Rate among all youth living in lower income households was 5 times higher compared with those living in highest income households, For Aboriginal persons in lowest income quintile, the rate was 3 times higher relative to non-Aboriginal persons in same quintille. Conclusion/ImplicationsNew linked health data reveal new information regarding the profile of those who experienced opioid-related adverse events. This information will serve to inform targeted intervention strategies, models for forward estimation of events.
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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 | 0.004 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".