Self‐Reported Practices in Opioid Management of Chronic Noncancer Pain: A Survey of Canadian Family Physicians
Bibliographic record
Abstract
BACKGROUND: In May 2010, a new Canadian guideline on prescribing opioids for chronic noncancer pain (CNCP) was released. To assess changes in family physicians' (FPs) prescribing of opioids following the release of the guideline, it is necessary to know their practices before the guideline was widely disseminated. OBJECTIVES: To determine FPs' practices and knowledge in prescribing opioids for CNCP in relation to the Canadian guideline, and to determine factors that hinder or enable FPs in prescribing opioids for CNCP. METHODS: An online survey was developed and FPs who manage CNCP were electronically contacted through the College of Family Physicians of Canada, university continuing medical education offices and provincial regulatory colleges. RESULTS: A total of 710 responses were received. FPs followed a precautionary approach to prescribing opioids and already practiced in accordance with Canadian guideline recommendations by discussing adverse effects, monitoring for aberrant drug-related behaviour and advising caution when driving. However, FPs seldom discontinued opioids even if they were ineffective and were unaware of the 'watchful dose' of opioids, the daily dose at which patients may need reassessment or closer monitoring. Only two of nine knowledge questions were answered correctly by more than 40% of FPs. The main enabler to optimal opioid prescribing was having access to a patient's opioid history from a provincial prescription monitoring program. The main barriers to optimal prescribing were concerns about addiction and misuse. CONCLUSIONS: While FPs follow a precautionary approach to prescribing opioids for CNCP, there are substantial practice and knowledge gaps, with implications for patient safety and costs.
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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 | 0.005 |
| 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) | 0.002 | 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".