Knowledge, Attitudes and Beliefs about Chronic Noncancer Pain in Primary Care: a Canadian Survey of Physicians and Pharmacists
Bibliographic record
Abstract
BACKGROUND: Primary care providers' knowledge, attitudes and beliefs (KAB) regarding chronic noncancer pain (CNCP) are a barrier to optimal management. OBJECTIVES: To evaluate and identify the determinants of the KAB of primary care physicians and pharmacists, and to document clinician preferences regarding the content and format of a continuing education program (CEP). METHOD: Physicians and pharmacists of 486 CNCP patients participated. Physicians completed the original version of the KnowPain-50 questionnaire. Pharmacists completed a modified version. A multivariate linear regression model was developed to identify the determinants of their KAB. RESULTS: A total of 137 of 387 (35.4%) physicians and 110 of 278 (39.5%) pharmacists completed the survey. Compared with the physicians, the pharmacists surveyed included more women (64% versus 38%) and had less clinical experience (15 years versus 26 years). The mean KnowPain-50 score was 69.3% (95% CI 68.0% to 70.5%) for physicians and 63.8% (95% CI 62.5% to 65.1%) for pharmacists. Low scores were observed on all aspects of pain management: initial assessment (physicians, 68.3%; pharmacists, 65.4%); definition of treatment goals and expectations (76.1%; 61.6%); development of a treatment plan (66.4%; 59.0%); and reassessment and management of longitudinal care (64.3%; 53.1%). Ten hours of reported CEP sessions increased the KAB score by 0.3 points. All clinicians considered a CEP for CNCP to be essential. Physicians preferred an interactive format, while pharmacists had no clear preferences. CONCLUSION: A CEP to improve primary care providers' knowledge and competency in managing CNCP, and to reduce false beliefs and inappropriate attitudes regarding CNCP is relevant and perceived as necessary by clinicians.
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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 | 0.001 | 0.000 |
| 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".