Identifying Office Resource Needs of Canadian Physicians to Help Prevent, Assess and Treat Patients with Substance Use and Pathological Gambling Disorders
Bibliographic record
Abstract
A study was conducted to determine whether there is a need for office-based resources to assist physicians in the prevention, assessment and/or management of patients, self and peers with substance use (excluding alcohol and tobacco) or pathological gambling disorders. The needs assessment was undertaken in three parts. Survey information was collected from 54 respondents including Executive Directors of the Canadian Medical Association's national affiliates and provincial/territorial Divisions, Deputy Ministers of Health, Registrars of provincial/territorial licensing bodies and Deans or Associate Deans of Continuing Medical Education programs in universities. Key informant interviews were conducted with 22 "experts" in the field of substance use and/or pathological gambling disorders. Focus groups were held in Vancouver, Toronto, Ottawa, Montreal and Halifax with 34 physicians who are interested in and whose caseload included patients with substance use and/or gambling disorders. Results suggest that current resources for both substance use and pathological gambling disorders are inadequate for physicians because there are gaps in the types activities and resources available, existing resources have not been effectively diffused or disseminated to physicians and training is needed to complement these resources to encourage proper implementation.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".