Disruptions in Care: Consequences of the COVID-19 Pandemic in a Children’s Hospital
Bibliographic record
Abstract
Abstract Background Public health restrictions are an essential strategy to prevent the spread of COVID-19; however, unintended consequences of these interventions may have led to significant delays, deferrals and disruptions in medical care. This study explores clinical cases where the care of children was perceived to have been negatively impacted as a result of public health measures and changes in healthcare delivery and access due to the COVID-19 pandemic. Methods This study used a qualitative multiple case study design with descriptive thematic analysis of clinician-reported consequences of the COVID-19 pandemic on care provided at a children’s hospital. A quantitative analysis of overall hospital activity data during the study period was performed. Results The COVID-19 pandemic has resulted in significant change to hospital activity at our tertiary care hospital, including an initial reduction in Emergency Department attendance by 38% and an increase in ambulatory virtual care from 4% before COVID-19, to 67% in August, 2020. Two hundred and twelve clinicians reported a total of 116 unique cases. Themes including (1) timeliness of care, (2) disruption of patient-centered care, (3) new pressures in the provision of safe and efficient care and (4) inequity in the experience of the COVID-19 pandemic emerged, each impacting patients, their families and healthcare providers. Conclusion Being aware of the breadth of the impact of the COVID-19 pandemic across all of the identified themes is important to enable the delivery of timely, safe, high-quality, family-centred pediatric care moving forward. What’s new COVID-19 disrupted typical paediatric care delivery. This study demonstrates the breadth of its’ impact on the delivery of timely, safe, equitable and patient and family centered care, highlighting considerations for paediatric providers as we move forward.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.005 |
| 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".