52 Cultivating Learner-centeredness in a Time of Rupture: Lessons from the Shift to Virtual Pediatric Academic Half Day during the COVID-19 Pandemic
Bibliographic record
Abstract
Abstract Primary Subject area Medical Education Background The COVID-19 pandemic has significantly disrupted the postgraduate learning environment. In light of public health recommendations and the need to offer safe learning environments, many programs have drawn upon virtual technologies to continue delivery of formal academic curricula. Despite widespread use, however, little is currently known as to how trainees view these changes. Objectives The authors sought to explore resident perceptions on the COVID-19-influenced shift from in-person to virtual academic half day (AHD) delivery. Design/Methods A cross-sectional survey was created and distributed to 51 pediatric residents who participated in virtual AHD at a university-affiliated Canadian program distributed across three sites, from March to June 2020. Survey responses were obtained confidentially through a secure, online platform (REDCap). Descriptive statistics and inductive thematic analysis were used to analyze responses to close-ended questions and narrative comments, respectively. Results Response rate was 60.8%. Residents reported statistically significant improvement in their attitudes towards virtual AHD across all metrics collected. Areas most strongly rated included increased trainee engagement and overall satisfaction with virtual delivery, in part due to increased relevance of content. Factors enabling participation included educationally safe interactions and a more comfortable and flexible learning environment. Conclusion These results suggest that the transition to virtual AHD was generally well received. During an uncertain time, when trainee vulnerability is heightened, the need to explicitly attend to educational issues of relevance, engagement, safety, and comfort are crucial. Further, given the rapid and reactive pivots to new curricular strategies in the wake of COVID-19, it is incumbent upon programs to incorporate resident feedback to ensure a learner-centred environment is maintained.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".