A survey on Canadian pediatric hospital clinical/medical teaching unit implementation during the first and second wave of the COVID-19 pandemic
Bibliographic record
Abstract
BACKGROUND: As the COVID-19 pandemic heightened, infection control and prevention experts recommended clinical training opportunities be modified or discontinued, substantially impacting the function of clinical or medical teaching units (CTU). A CTU is structured to involve medical learners such that they become active participants of the health care team. Since a review of the literature demonstrates a paucity of data to guide pediatric CTU implementation during pandemic phases, we developed and disseminated a survey to assess Canadian practices. METHOD: A group of infectious disease specialists and pediatric hospitalists developed, tested, and disseminated surveys to understand CTU clinical rounding and teaching practices during the waves of the COVID-19 pandemic. RESULT: Our surveys demonstrate the variability in adapting rounding practices during this pandemic and highlights the opportunities to share our approaches and lessons learned to optimize learner experience and patient centered care during unprecedented times in our academic hospitals. We also show the pragmatic implementation of our new pediatric hospital CTU process that was informed by our survey results. CONCLUSION: Our study demonstrates the variability in adapting rounding practices during this pandemic and highlights the opportunities to share our approaches and lessons learned to optimize learner experience and patient centered care during unprecedented times in our academic hospitals.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".