Knowledge, Perceptions and Attitudes toward Chronic Pain and Its Management: A Cross-Sectional Survey of Frontline Pharmacists in Ontario, Canada
Bibliographic record
Abstract
The treatment of chronic pain consumes a significant share of primary care. Community and family health team pharmacists frequently see patients with chronic pain, thus have the opportunity to improve their care. To assess the knowledge, perceptions, and attitudes of Ontario pharmacists, we invited 5,324 Ontario pharmacists, to participate in an online survey we developed using Qualtrics. The 31-question survey gathered demographic information, assessed pharmacists' knowledge of three chronic pain conditions; chronic lower back pain (CLBP, eight true/false statements); chronic headache disorder (CHD, eight true/false statements) and painful diabetic neuropathy (PDN, seven true/false statements), and their attitudes toward and perceptions of patients with these conditions, and knowledge, attitudes, and perceptions of opioids in pain management. We received 688 responses (12.9%) and 392 pharmacists completed the survey. The mean age of respondents was 48.5 years and 48.5% were male. More than 50% of respondents were in practice for more than 20 years and 58.7% worked 25-40 hours per week. The mean knowledge scores were 4.5/8, 5.5/8, and 5.3/8 for CBLP, CHD, and PDN respectively. While 95% of respondents were aware of the increasing death rates due to opioid use, only half were familiar with the Canadian guideline for safe opioid prescribing for non-cancer use. Responses were compared based on gender, time in practice and location of practice. Pharmacists with more than ten years of experience scored significantly higher than those with less experience. Fewer differences were found in comparisons of gender and location of practice. Safe and effective care of chronic pain patients, particularly with opioids, will require additional pharmacist education.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".