The Secondary Consequences of the COVID-19 Pandemic in Hospital Pediatrics
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The coronavirus disease (COVID-19) pandemic has broad implications for children and families. Particular attention has been paid to delays in accessing timely pediatric care leading to unintended morbidity. In this study, we aim to describe the broader spectrum of unintended negative consequences for pediatric patients and families due to recent health care and societal changes. METHODS: All full-time doctors, dentists, and nurse practitioners working at a tertiary care children's hospital in Canada were surveyed every 2 weeks throughout the initial phase of the COVID-19 pandemic to identify clinical cases in which they perceived a negative outcome associated with hospital or societal changes as a result of the COVID-19 pandemic. Analysis followed a qualitative case series methodology using a narrative synthesis approach to determine similarities and associated themes. RESULTS: One hundred and forty-one clinicians, representing 26 hospital divisions, reported 57 unique cases in the first 6 weeks of the study. Thematic analysis of the first 50 reported cases was used to identify 6 primary themes focusing on health care quality domains as described by the Agency for Healthcare Research and Quality (safe, effective, patient-centered, timely, efficient, and equitable care). CONCLUSIONS: In this preliminary case analysis, we describe the broad social and clinical impact of COVID-19 on hospitalized pediatric patients and their families. These themes highlight the unintended consequence on families, siblings, disease diagnosis, and hospital-based care provision. Recognition and understanding of the broad implications of the COVID-19 pandemic are necessary as we strive to deliver safe, high-quality, family-centered pediatric care in this new era.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 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".