A Survey of Cancer Pain Management Knowledge and Attitudes of British Columbian Physicians
Bibliographic record
Abstract
INTRODUCTION: There are many potential barriers to adequate cancer pain management, including lack of physician education and prescription monitoring programs. The authors surveyed physicians about their specific knowledge of pain management and the effects of the regulation of opioids on their prescribing practices. METHODS: A questionnaire was mailed out to British Columbia physicians who were likely to encounter cancer patients. The survey asked for physicians' opinions about College of Physicians and Surgeons of British Columbia regulation and other issues related to their prescribing practices, and assessed basic knowledge of cancer pain management. RESULTS: There was a 69% return rate with a total of 4618 evaluable responses. There was a significant difference among medical disciplines, years in practice, number of chronic pain patients seen and size of community of practice. The highest knowledge scores were achieved by oncologists and the lowest scores were from surgeons. Those who practiced in smaller communities had a higher average knowledge score. Those who felt their knowledge about cancer pain was inadequate scored lower than those who felt their knowledge was adequate. The questions most frequently answered incorrectly (or by "don't know") were those about equianalgesic dosing (68%) and adequate breakthrough dosing (45%), revealing knowledge deficiencies that would significantly impair a physician's ability to manage cancer pain. CONCLUSIONS: The details of opioid prescribing are crucial areas to target education for cancer pain management. The surveyed physicians accepted the need for regulation of opioid prescribing with very few being fearful of scrutiny from the College of Physicians and Surgeons of British Columbia. However, the inconvenience of the triplicate prescription pad was more of a barrier to prescribing, it being of concern to 20% of respondents, particularly surgeons and medical specialists.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".