Tracking Trends of Alcohol, Illicit Drugs and Tobacco through Morbidity Data
Bibliographic record
Abstract
Despite various national and provincial tobacco, alcohol, and illicit drug surveys in Canada, tracking trends and patterns of use is difficult. These surveys often target specific populations and are prone to sampling or respondent bias. This article describes a feasibility study to provide alcohol-, illicit drug- and tobacco-related morbidity using hospital separation data. Hospital episodes for diseases and conditions wholly or partially attributable to alcohol, illicit drugs, and tobacco by health authority, age group, sex, and specific ICD-10 codes for British Columbia (BC) were obtained. The most responsible diagnosis statistics were combined with aetiologic fractions for each ICD-10 code to estimate the total burden of substance use by health authority. Hospital admissions attributable to alcohol and tobacco each cause approximately 3 and 5 times respectively, that attributable to illicit drugs. The ongoing analysis of morbidity data will be used to inform the health authorities, and to assist policy makers in creating and evaluating policies.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".