A Survey of Knowledge, Attitudes, and Behaviours of Canadian Family Physicians in Response to the Canadian Guideline on Prescribing Opioids for Chronic Non-cancer Pain
Bibliographic record
Abstract
Chronic Non-Cancer Pain (CNCP) is prevalent in Canada. Family Physicians (FPs) are providing essential care for patients living with CNCP, including opioid stewardship. Opioid prescribing rates in Canada remain high. This thesis discusses three Canadian surveys of FPs conducted in 2010, 2018 and 2024-25. The thesis uncovers barriers and enablers to optimal opioid prescribing to manage CNCP. Design: Three nation-wide surveys, cross-sectional design. Results: Due to significant geographical and demographic variations in the distribution of the three surveys, the results cannot be directly compared. The thesis reports each survey result separately. The results are presented narratively, and only descriptive statistics are offered. Participants were mainly females (52%) and mostly from Ontario (50%). Of them, 44% reported practicing for more than 20 years, and 41% said were confident in prescribing opioids for CNCP. Conclusion: Overall, several knowledge gaps and a lack of adherence to certain guideline-recommended practices were observed among respondents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| 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) | 0.004 | 0.001 |
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".