Staying Online in Uncertain Times: A Nationwide Canadian Survey of Pathology Resident Uses of and Adaptations to Online Learning During COVID-19
Bibliographic record
Abstract
CONTEXT.—: Because of restrictions as a result of the COVID-19 pandemic, medical educators rapidly transitioned to an online curriculum for pathology resident education. The benefits and challenges of the shift to online learning, as well as strategies to maximize learning, are yet to be fully elucidated. OBJECTIVE.—: To assess learner perception and satisfaction with the move to online learning. Understanding the benefits of online learning will allow future curricular changes to most effectively incorporate online learning. Understanding the common challenges will allow our current learning strategies to rapidly adapt and ideally mitigate these challenges as online learning is incorporated into medical education. DESIGN.—: This was a survey-based study distributed by email to pathology residents nationwide in Canada in anatomic pathology, general pathology, neuropathology, and hematopathology. Thirty residents participated, from anatomic pathology (n = 23; 76%), from general pathology (n = 5; 16%), and 1 participant each from hematopathology and neuropathology. RESULTS.—: All participants indicated that their program had transitioned to online learning at least in part. The majority of participants (n = 16; 53%) did not feel their pathology education was negatively affected by the transition to online learning; however, a significant minority (n = 6; 20%) felt their education had been negatively affected. Convenience and less intimidation were rated as benefits of online learning. Negative effects included technical issues and decreased engagement; we identified a number of strategies used by programs and pathology residents to mitigate these negative effects. CONCLUSIONS.—: Our survey points to a need to use adaptations and best-practice recommendations to maximize the benefits of online learning moving forward.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".