Factors Associated with Physiotherapists’ Interest in Cardiorespiratory Continuing Education Using Computer-Assisted Learning: A Survey
Bibliographic record
Abstract
PURPOSE: To determine factors associated with Canadian physiotherapists' interest in undertaking continuing education in various cardiorespiratory content areas and their willingness to complete a portion of study within each of these content areas via computer-assisted learning (CAL). METHODS: In a six-page mailed questionnaire, 1,426 potential participants were asked to indicate their interest in 11 cardiorespiratory content areas, their continuing-education preferences, and their access and willingness to do continuing education by CAL. Demographic data were also collected from respondents. RESULTS: Respondents included 285 physiotherapists from cardiorespiratory interest groups (CRGs) and 447 from the licensing bodies' sample (overall response rate = 56%). Physiotherapists in public employment and practice areas other than orthopaedics had increased interest in all cardiorespiratory content areas except Exercise Physiology. Membership in a CRG increased their likelihood to be willing to learn the cardiorespiratory content area via CAL. CONCLUSIONS: In developing content and determining the accessibility of cardiorespiratory continuing education, educators should consider the type of employer and area of practice of interested attendees as well as the lack of willingness to use CAL by those not involved in CRGs.
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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 | 0.006 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".