Characteristics and outcomes of doctors in a substance dependence monitoring programme in Canada: prospective descriptive study
Bibliographic record
Abstract
OBJECTIVE: To describe the characteristics at enrollment and outcomes of doctors in a substance dependence monitoring programme in Canada. DESIGN: Prospective descriptive study. SETTING: Provincial physician health programme, Canada. PARTICIPANTS: All 100 doctors consecutively admitted to a substance dependence monitoring programme and followed until completion of monitoring or on leaving the programme. MAIN OUTCOME MEASURE: Relapse during long term monitoring for five years. RESULTS: Ninety per cent of the doctors enrolled on the programme were men, 66% were married or living with a partner, 44% had had previous treatment for substance dependence, and 36% had had previous psychiatric treatment. Smokers were over-represented compared with the general population of US doctors (38% v 5%). During the monitoring period 71% of participants had no known relapse. An additional 14% went on to complete the programme, after some form of relapse. In total, 85% of the doctors successfully completed the programme. CONCLUSION: In this cohort of doctors enrolled on the Ontario Physician Health Program for substance dependence, most were men who were dependent on alcohol or opioids. Smokers were over-represented compared with the general population of US doctors. Eighty five per cent successfully completed the programme.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".