Continuing professional development needs in pain management for Canadian health care professionals: A cross sectional survey
Bibliographic record
Abstract
Continuing professional development is an important means of improving access to effective patient care. Although pain content has increased significantly in prelicensure programs, little is known about how postlicensure health professionals advance or maintain competence in pain management. The aim of this study was to investigate Canadian health professionals’ continuing professional development needs, activities, and preferred modalities for pain management. This study employed a cross-sectional self-report web survey. The survey response rate was 57% (230/400). Respondents were primarily nurses (48%), university educated (95%), employed in academic hospital settings (62%), and had ≥11 years postlicensure experience (70%). Most patients (>50%) cared for in an average week presented with pain. Compared to those working in nonacademic settings, clinicians in academic settings reported significantly higher acute pain assessment competence (mean 7.8/10 versus 6.9/10; P < 0.002) and greater access to pain specialist consultants (73% versus 29%; P < 0.0001). Chronic pain assessment competence was not different between groups. Top learning needs included neuropathic pain, musculoskeletal pain, and chronic pain. Recently completed and preferred learning modalities respectively were informal and work-based: reading journal articles (56%, 54%), online independent learning (44%, 53%), and attending hospital rounds (43%, 42%); 17% had not completed any pain learning activities in the past 12 months. Respondents employed in nonacademic settings and nonphysicians were more likely to use pocket cards, mobile apps, and e-mail summaries to improve pain management. Canadian postlicensure health professionals require greater access to and participation in interactive and multimodal methods of continuing professional development to facilitate competency in evidence-based pain management.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".