Attitudes Toward Opioid Use for Chronic Pain: A Canadian Physician Survey
Bibliographic record
Abstract
OBJECTIVES: To measure chronic pain patient volumes seen in primary care practice; to determine what medications physicians choose for the treatment of moderate to severe chronic pain; to identify barriers to the use of opioids in the treatment of chronic pain; and to assess physicians' attitudes toward the current management of chronic pain in Canada. DESIGN: A computer-assisted telephone survey of 100 regionally representative Canadian physicians with a defined interest in palliative care (PC, n=30) or noncancer pain (GP, n=70). SETTING: A survey was conducted by Ipsos-Reid in June 2001. Only physicians who met the eligibility criteria of having written 20 or more prescriptions for moderate to severe pain in the preceding four weeks or having devoted 20% of time to palliative care were eligible to participate. RESULTS: In one month, the average number of patients with moderate to severe chronic pain seen by PCs was 94.2; the average seen by GPs was 44.7. The pain experienced by 83.3% of GP patients was noncancer related. For chronic cancer pain, an opioid analgesic was the treatment of choice of 79% of physicians (48% preferred morphine, 21% codeine, 10% other). For moderate to severe chronic noncancer pain, opioids were the first-line treatment of only 32% of physicians (16% preferred codeine, 16% major opioids) because a significant number preferred either non-steroidal anti-inflammatory drugs (29%) or acetaminophen (16%). Thirty-five per cent of GPs and 23% of PCs would never use opioids for noncancer pain, even when described as severe. Chronic pain was deemed by 68% of physicians to be inadequately managed. Almost 60% thought that pain management could be enhanced by improved physician education. Identified barriers to opioid use included addiction potential (37%) and side effects (25%). Seventeen per cent of GPs and 10% of PCs thought that regulatory sanctions limited opioid prescribing. CONCLUSIONS: Even among physicians experienced in chronic pain treatment, there is a reluctance to use opioids for severe nonmalignant pain. One-half of the survey participants believed that there was a need for improved physician education in pain management, including the use of opioids.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".