Exploring Virtual Teaching Approaches Among Pediatricians During the SARS-CoV-2 Pandemic: A Virtual Ethnographic Study
Bibliographic record
Abstract
INTRODUCTION: During the SARS-CoV-2 pandemic, Canadian postsecondary institutions were forced to rely on online teaching to comply with physical distancing recommendations. This sole reliance on virtual methods to deliver synchronous teaching sessions in medical education was novel. We found little empirical research examining pediatric educators' experiences. Hence, the objective of our study was to describe and gain a deeper understanding of pediatric educators' perspectives, focusing on the research question, "How is synchronous virtual teaching impacting and transforming teaching experiences of pediatricians during a pandemic?" METHODS: A virtual ethnography was conducted guided by an online collaborative learning theory. This approach used both interviews and online field observations to obtain objective descriptions and subjective understandings of the participants' experiences while teaching virtually. Pediatric educators (clinical and academic faculty) from our institution were recruited using purposeful sampling and invited to participate in individual phone interviews and online teaching observations. Data were recorded and transcribed, and a thematic analysis was conducted. RESULTS: Fifteen frontline pediatric teachers from our large Canadian research-intensive university were recruited. Four main themes, with subthemes, emerged: (1) the love/hate relationship with the virtual shift; (2) self-imposed pressure to increase virtual engagement; (3) looking back, moving forward; (4) accelerated adaptation and enhanced collaboration. CONCLUSION: Pediatricians adopted new delivery methods quickly and found many efficiencies and opportunities in this shift. Continued use of virtual teaching will lead to increased collaboration, enhanced student engagement strategies, and blending the advantages of virtual and face-to-face learning.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".