Continuing Education of Physiotherapists Involved in Treating Persons with Work-Related Back Pain
Bibliographic record
Abstract
Purpose: Physiotherapists often use continuing education (CE) courses to acquire new knowledge, but little is known about the types and combinations of CE courses attended. The objective of this study was to describe the CE “profiles” of physiotherapists who treat individuals with work-related back pain. Methods: Physiotherapists answered a self-administered questionnaire about the CE courses attended after initial training. They also provided demographic information, including years of practice, university of graduation, and distance of workplace from urban centres. Multiple correspondence analysis with hierarchical classification was used to identify CE profiles, that is, combinations of courses most frequently attended by physiotherapists in this group. Results: Responses were received from 332 physiotherapists (response rate of 81.4%) working in 199 randomly selected clinics in Quebec. A very high proportion (88.9%) reported having attended at least one CE course related to back pain treatment. The most common area of CE was mobilization (53.9% of therapists). Others included osteopathy (21.7%), varied CE (15.9%), and neural mobilization/muscle reeducation (8.5%). Years of practice and university of graduation were significantly related (p < .05) to CE profiles, whereas distance from urban centres was not. Conclusions: The variations in CE profiles among physiotherapists treating the same type of clientele raise questions regarding the potential for important differences in the knowledge base used to treat workers with back pain. More effective CE strategies are needed to promote evidencebased knowledge among physiotherapists involved in back pain treatment for injured workers.
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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 | 0.009 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".