Implementation of Interdisciplinary Province-Wide Webinar Series During the COVID-19 Pandemic by the Federation of Medical Specialists of Quebec (FMSQ): A Survey Study
Bibliographic record
Abstract
Objectives: COVID-19 has forced a transformation in continuing professional development (CPD), shifting to virtual platforms. We report the results of a rapidly-implemented COVID-19 online interdisciplinary CPD webinar series. We aimed to determine if this virtual approach for large-scale CPD was relevant, appreciated, and effective for specialist physicians in Quebec. Methods and Analysis: This was a retrospective descriptive online survey-based study. The weekly virtual educational webinars took place between March 3, 2020 to June 15, 2020, resulting in a total of 26 webinars over 16 weeks. The study included all individuals who attended any of the webinar sessions, namely specialist physicians and department chiefs. Number of participants and overall appreciation of webinar sessions were data points collected. Results: Across all webinars, there were 8,500 unique specialist physicians which comprises 80.7% of the entire specialist practicing population in Quebec. Of note, every medical and surgical specialty was represented by attendance in at least one session. In total, 27,504 evaluation forms were completed out of all the sessions, meaning a 78.4% response rate. In post-webinar surveys, 97.6% of respondents agreed or strongly agreed that the webinars were pertinent to their practice and 94.6% agreed or strongly agreed that the presentation met their continuing professional needs. Conclusions: This novel interdisciplinary COVID-19 webinar series is a successful and appreciated strategy to maintain CPD amidst a global pandemic. One year later, it has become a mainstay in our toolbox and we trust this unique model of large-scale interdisciplinary CPD via webinar sessions is useful in normal times as well as in times of crisis.
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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 | 0.003 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".