89 The Unintended Consequences and Impact of the COVID-19 Pandemic on Patients, Families and Clinicians in a Children’s Hospital
Bibliographic record
Abstract
Abstract Primary Subject area Hospital Paediatrics Background The coronavirus (COVID-19) pandemic has broad implications for children and families. Healthcare experience and delivery has changed significantly, and changes will likely continue for some time. Particular attention has been paid to delays in accessing timely pediatric care leading to unintended morbidity. Objectives This study aimed (1) to describe the broader spectrum of unintended negative consequences by describing the courses of care altered by the COVID-19 pandemic from the clinician's perspectives and (2) to identify thematic similarities to inform clinical practice change. Design/Methods All full-time doctors, dentists, and nurse practitioners working at a tertiary care children’s hospital in Canada were surveyed every two weeks throughout the initial phase of the COVID-19 pandemic. We asked them to identify and describe clinical cases in which they perceived a negative outcome associated with hospital or societal changes due to the COVID-19 pandemic. Analysis followed a qualitative case series methodology using a narrative synthesis approach to determine similarities and associated themes. Results Two-hundred and twelve clinicians reported 116 cases. Several broad themes emerged, including (1) timeliness of care, (2) disruption of child and family-centred care, (3) new pressures in the provision of safe and efficient care and (4) inequity in the experience of the COVID-19 pandemic. Within each of these themes, subthemes emerged, highlighting its impact on (1) patients, (2) their families and (3) healthcare providers. Table 1 provides examples of cases within each theme. Conclusion The broad consequences of the COVID-19 pandemic impact patients, families, healthcare providers and the healthcare system. Understanding this breadth is necessary as we strive to deliver safe, high quality, family-centred pediatric care in this new era. As the pandemic continues, we need to consider carefully how to provide elective and ambulatory care, including surgery, in this era of social distancing. Particular attention is needed to understand particular aspects, including vulnerable children and the clinician experience of the COVID-19 pandemic.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".