Screening and Treatment for Alcohol, Tobacco and Opioid Use Disorders: A Survey of Family Physicians across Ontario
Bibliographic record
Abstract
INTRODUCTION: As a primary point of contact within the health care system, family physicians are able to play a vital role in identifying individuals with substance use disorders and connecting them to the appropriate treatment. However, there is very little data available on whether family physicians are actively screening for and treating substance use disorders. The objective of the current survey was to assess whether family physicians in Ontario are screening for alcohol, opioid and tobacco use disorders, using validated tools and providing treatment. METHODS: An online survey consisting of a series of 38 primarily close-ended questions was circulated to family physicians in Ontario. Rates of screening for alcohol, opioid and tobacco dependence, use of validated tools for screening, providing treatment for dependent individuals and the current barriers to the prescription of pharmacotherapies for these drug dependences were assessed. RESULTS: The use of validated screening tools was limited for all three substances. Screening by family physicians for the substance use disorders among adolescents was much lower than screening among adults. Pharmacotherapy was more commonly used as an intervention for tobacco dependence than for alcohol and opioid dependence. This was explained by the lack of knowledge among family physicians on the pharmacotherapies for alcohol and opioid dependence. CONCLUSIONS: Findings from the current study suggest there is a need for family physicians to integrate screening for substance use disorders using validated tools into their standard medical practice. Furthermore, there is a need for increased knowledge on pharmacotherapies for alcohol and opioid use disorders. It is important to note that the low response rate is a major limitation to this study. One possible reason for this low response rate may be a lack of interest and awareness among family physicians on the importance of screening and treatment of substance use disorders in Ontario.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".