Illegal drug‐attributable morbidity in Canada 2002
Bibliographic record
Abstract
Use of illegal drugs is an important behavioral risk factor for burden of morbidity in developed countries. The objective was to estimate the number of diagnoses in acute care hospitals, psychiatric hospitalizations, admissions in specialized treatment, and number of days in treatment attributable to use of illegal drugs for Canada in 2002. The number of diagnoses in acute care hospitals, psychiatric hospitalizations, and hospital days were obtained from the Canadian Institute for Health Information (CIHI). Number of admissions and number of days in specialized inpatient and outpatient treatment of illegal drug dependency were obtained from provincial ministerial officials or drug addiction program coordinators. Except for effects of maternal use of drugs of addiction on the newborn, and suicide, drug-attributable fractions (DAFs) were estimated directly from available statistics in published literature. There were 61,026 illegal drug-related diagnoses in acute care hospitals, 1,517 psychiatric hospitalizations, and 139,773 admissions to specialized treatment attributable to illegal drug use in Canada. The largest contributors were mental and behavioral disorders due to psychoactive substance use in acute care hospitals, and drug psychoses in psychiatric hospitalizations. Length of stay amounted to 352,121 days in acute care hospitals, 31,508 days in psychiatric hospitals, and 2,851,829 days in specialized treatment. Drug use constitutes a major contributor to burden of morbidity in Canada. Compared to 1992, the total number of illegal drug-attributable days in 2002 increased, especially in acute hospitals by a factor of 9.6. A mixture of prevention and harm reduction measures is proposed to reduce the burden of morbidity associated with drug use.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".