Perceived Advantages and Disadvantages of Online Continuing Professional Development (CPD) During COVID-19: CPD Providers' Perspectives
Bibliographic record
Abstract
ABSTRACT: COVID-19 precipitated many CPD providers to develop new technology competencies to create effective online CPD. This study aims to improve our understanding of CPD providers' comfort level, supports, perceived advantages/disadvantages, and issues in technology-enhanced CPD delivery during COVID-19. A survey was distributed to CPD providers at the University of Toronto and members of the Society for Academic Continuing Medical Education and analyzed using descriptive statistics. Of the 111 respondents, 81% felt very to somewhat confident to provide online CPD, but less than half reported IT, financial, or faculty development supports. The top reported advantage to online CPD delivery was reaching a new demographic; top disadvantages included videoconferencing fatigue, social isolation, and competing priorities. There was interest in using less frequently used educational technology such as online collaboration tools, virtual patients, and augmented/virtual reality. COVID-19 precipitated an increased comfort level in using synchronous technologies to provide CPD, giving the CPD community an increased cultural acceptance and skill level to build on. As we move beyond the pandemic, it will be important to consider ongoing faculty development, particularly toward asynchronous and HyFlex delivery methods to continue expand CPD reach and negate negative online experiences such as videoconferencing fatigue, social isolation, and online distractions.
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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 | 0.006 | 0.031 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".