Learner’s Perspectives of Free, Online, International Continuing Medical Education in Rehabilitation
Bibliographic record
Abstract
ABSTRACT: The COVID-19 pandemic spurred global engagement with continuing medical education. The Canadian Advances in Neuro-Orthopedics for Spasticity Consortium's free online platform offering interdisciplinary expert lectures on spasticity saw parallel growth. We analyzed 1733 responses from 41 post-session surveys to assess the learner's perspectives of online continuing medical education using a convergent mixed-methods design. The qualitative analysis produced four themes: [1] event value and satisfaction (subthemes: quality and impact of speakers , accessibility of the online format , discussions and interactions, and the benefits of visual learning ), [2] increased competence (subthemes: increased knowledge, intent to apply , and increased confidence), [3] inspiring collaboration (subthemes: need for multidisciplinary teams, international collaboration , and effective communication tools ), [4] considerations and recommendations (subthemes: relevance to developing countries , technical aspects , and academic level of content) . Quantitative analyses supported these findings, showing high levels of satisfaction and perceived gains in knowledge. Notably, 88% of participants indicated intent to apply their knowledge, and 84% stated that it would enhance their competence. The results underscore the importance of interaction in online education and highlight a need for communication skills training to facilitate multidisciplinary teamwork. The findings revealed disparities in perceptions of the academic difficulty of continuing medical education, which warrants investigation into participants' selection of continuing medical education webinars.
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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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".