Attitudes toward the Canadian Guideline for Safe and Effective Use of Opioids for Chronic Non-Cancer Pain: A qualitative study
Bibliographic record
Abstract
BACKGROUND: Chronic noncancer pain (CNCP) refers to all pain disorders, not due to cancer, that persist for ≥3 months. The point prevalence of CNCP in the general population of Western countries is between 19 and 33 percent. Opioids are commonly prescribed for CNCP and are associated with both benefits and harms. The Canadian Guideline for Safe and Effective Use of Opioids for CNCP was published in 2010 to provide guidance for optimal opioid prescribing in patients with CNCP. OBJECTIVES: To investigate the attitudes toward, and use of, the Canadian Opioids Guideline among pain physicians. DESIGN: A qualitative study using one-on-one, semistructured interviews with 12 pain physicians in Ontario, Canada, and thematic analysis of verbatim transcripts. RESULTS: Major themes that emerged from interviews included: (1) generally positive attitudes toward the 2010 Canadian Opioids Guideline, but limited use-half (six of 12) reported they did not use the guideline in practice; (2) strongly contrasting views regarding the 200 mg/d morphine equivalent watchful dose; (3) recognition of gaps in the guideline, especially recommendations for urine drug screening and pain severity-specific therapy; (4) the guideline is excessively long and the format suboptimal; and (5) improved dissemination and education are needed to enhance guideline uptake. CONCLUSIONS: Despite its merits, the Canadian Opioids Guideline suffers from information gaps and from limited uptake, at least in part due to suboptimal format and suboptimal dissemination.
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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".