COVID-19 Pandemic: Perspective From Italian Pediatric Emergency Physicians
Bibliographic record
Abstract
OBJECTIVES: To document the lived experience of Italian pediatric emergency physicians during the coronavirus disease 2019 (COVID-19) pandemic. METHODS: We developed a structured interview to collect the lived experience of the staff of the pediatric emergency department (PED) of a tertiary referral university hospital in Northern Italy. The open-ended questions were draft according to the suggestions of Canadian colleagues and administered by 1 interviewer, who was part of the PED staff, at the end of March 2020. All the PED staff was interviewed, on a voluntary basis, using purposive sampling. RESULTS: Most respondents declared to be afraid of becoming infected and of infecting their families. The number of patients seen in the PED has decreased, and the cases tend to be more severe. A shift in the clinical approach to the ill child has occurred, the physical examination is problem-oriented, aiming to avoid un-necessary maneuvers and to minimize the number of practitioners involved. The most challenging aspects reported are: (1) performing a physical examination in personal protective equipment (PPE), (2) being updated with rapidly evolving guidelines, and (3) staying focused on the possible COVID-19 clinical presentation without failing in differential diagnosis. CONCLUSIONS: During the COVID-19 pandemic, it seems that pediatric emergency physicians are radically changing their clinical practice, aiming at prioritizing essential interventions and maneuvers and self-protection.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".