Reflecting back to move forward: Lessons learned about <scp>COVID</scp>‐19 safety protocols from pediatric anesthesiologists
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic brought about the immediate need for enhanced safety protocols in health care centers. These protocols had to evolve as knowledge and understanding of the disease quickly broadened. AIMS: Through this study, the researchers aimed to understand the experiences of pediatric anesthesiologists at the Montreal Children's Hospital and the Shriners' Hospital Canada as they navigated the first wave of COVID-19 at their institutions. METHODS: Nine participants from the Montreal Children's Hospital and the Shriners' Hospital were interviewed. Interviews were recorded, transcribed verbatim, and then analyzed using an applied philosophical hermeneutics approach. FINDINGS: Participants expressed their wish for simple and easy-to-apply protocols while recognizing the challenge of keeping up with evolving knowledge on the disease and its transmission. They pointed to some limitations and unintended consequences of the safety protocols and the system-wide flaws that the COVID-19 pandemic helped bring to light. They described their frustrations with some aspects of the safety protocols, which they at times felt could be more efficient or better suited for their daily practice. CONCLUSIONS: The findings of this study highlighted the importance of listening to and empowering anesthesiology staff working in the field during crises, the implications of shifting from patient-centered care to community-centered care, and the fine line between sharing as much emerging information as possible and overwhelming staff with information.
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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 | 0.073 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.029 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.006 | 0.016 |
| Insufficient payload (model declined to judge) | 0.003 | 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".