Creating meaning through small doses of actionable learning: A mixed methods analysis of CAP-ACP’S virtual CME activities
Bibliographic record
Abstract
Background: The COVID-19 pandemic overwhelmed pathology services, halting routine case reviews and disrupting resident training. In response, the Canadian Association of Pathologists – Association canadienne des pathologistes (CAP-ACP) launched virtual one-hour webinars, allowing pathologists to learn while working and ensuring residents remained exposed to essential cases despite pandemic constraints. Methods: Using a convergent mixed-methods design, data from 115 post-session evaluation surveys were analyzed. Participants included pathologists and pathologists’ assistants, with 93% of responses from Canada. Quantitative and qualitative data were analyzed separately. Qualitative analysis employed an inductive thematic approach using a codebook, with member checking completed by two pathologists. Results: High satisfaction with the learning activities was reported. Three main themes emerged: participants viewed the webinars as accessible, engaging, and impactful; the learning was actionable and relevant to daily practice – improving report writing, data interpretation, diagnostic accuracy, and tissue handling – while also equipping lecturers to teach effectively online; and pathology at a crossroads. Quantitative responses showed that 80% felt the webinar enhanced their competence, but only 66% believed it would impact patient outcomes, suggesting a disconnect between their work and its perceived clinical impact. The webinars reminded pathologists of the clinical significance of their work and reflected a desire to connect more with colleagues through multidisciplinary collaboration. There was also a strong call to engage leadership in addressing burnout as a shared responsibility. Webinar platforms can support both skill-building and meaningful professional dialogue. Discussion: The results highlight the multifaceted value of virtual continuing medical education: enhancing diagnostic, teaching, and leadership skills while helping pathologists reconnect with meaning and purpose in their work. As the profession navigates post-pandemic challenges, webinar platforms offer a scalable tool to support clinical competence, collaboration, and engagement with systemic issues such as burnout and professional identity.
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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 | 0.074 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".