Canadian Physical Therapists' Interest in Web-Based and Computer-Assisted Continuing Education
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Distance education via computer-assisted learning (CAL), including Web-based and CD-ROM learning, confers a number of advantages compared with traditional learning methods. The purposes of this study were (1) to determine the interest of Canadian physical therapists in participating in continuing education using CAL methods and (2) to determine whether interest in CAL was related to type of employment, area of practice, education, computer skill and access, and other demographic variables. SUBJECTS AND METHODS: A random sample of Canadian physical therapists and all members of cardiopulmonary interest groups were surveyed. RESULTS: Of 1,426 survey questionnaires mailed, 69 were returned (58 were unopened and 11 were duplicates). From the remaining 1,357 potential survey responses, 757 responses were received, for an overall response rate of 56%. Seventy-eight percent of the respondents indicated their interest in participating in CAL. Factors associated with interest in CAL included 2 or more hours of Internet access per week, Internet access at both home and work, computer skill, education level, practice area, and belonging to a cardiopulmonary interest group. DISCUSSION AND CONCLUSION: The findings indicate a large positive interest in CAL. Increasing CAL continuing education opportunities could increase options for physical therapists to meet professional expectations for continuing competency.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".