Barriers and facilitators encountered by family physicians prescribing opioids for chronic non-cancer pain: a qualitative study
Bibliographic record
Abstract
INTRODUCTION: Harms caused by prescription opioid analgesics (POAs) have been identified as a major international public health concern. Recent statistics show rising numbers of opioid-related deaths across Canada. However, Canadian family physicians appear to have inadequate resources to safely and effectively prescribe opioid analgesics to treat chronic non-cancer pain (CNCP). METHODS: We completed a qualitative study of the barriers and facilitators to safe and effective prescribing of opioid analgesics for CNCP through semi-structured interviews with eight family physicians in Nova Scotia. Thematic analysis was used to identify the barriers and facilitators. RESULTS: Family physicians identified challenges in prescribing opioid analgesics for CNCP: the complexity of CNCP management, addictions risks and prescribing tools, physician training, the physician-patient relationship, prescription monitoring and control, and systemic factors. CONCLUSION: Family physicians described themselves as inadequately supported in their prescribing of opioid analgesics for CNCP and could benefit from an integrated and coordinated approach to prescriber support.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".