Experience of Pediatricians and Pediatric Surgeons With Virtual Care During the COVID-19 Pandemic: Descriptive Study
Bibliographic record
Abstract
BACKGROUND: Prior to the COVID-19 pandemic, in-clinic visits were the standard of care for pediatric physicians and surgeons at our center. At the pandemic onset, web-based care was adopted at an unprecedented scale and pace. OBJECTIVE: This descriptive study explores the web-based care experience of pediatric physicians and surgeons during the pandemic by determining factors that supported and challenged web-based care adoption. METHODS: This study took place at the Children's Hospital at London Health Sciences Centre, a children's hospital in London, Ontario, Canada, which provides pediatric care for patients from the London metropolitan area and the rest of Southwestern Ontario. The Donabedian model was used to structure a web-based survey evaluating web-based care experience, which was distributed to 121 department-affiliated pediatric physicians (including generalists and subspecialists in surgery and medicine). Recruitment occurred via department listserv email. Qualitative data were collected through discrete and free-text survey responses. RESULTS: Survey response rate was 52.1% (63/121). Before the pandemic, few physicians within the Department of Paediatrics used web-based care, and physicians saw <10% of patients digitally. During March-May 2020, the majority transitioned to web-based care, seeing >50% of patients digitally. Web-based care use in our sample fell from June to September 2020, with the majority seeing <50% of patients digitally. Telephone and Ontario Telemedicine Network were the platforms most used from March to September 2020. Web-based care was rated to be convenient for most providers and their patients, despite the presence of technical difficulties. Challenges included lack of physical exam, lower patient volumes, and poor patient digital care etiquette. Regardless of demographics, 96.4% (116/121) would continue web-based care, ideally for patients who live far away and for follow-ups or established diagnoses. CONCLUSIONS: Transition to web-based care during COVID-19 was associated with challenges but also positive experiences. Willingness among pediatricians and pediatric surgeons to continue web-based care was high. Web-based care experiences at our center could be improved with patient education and targeting select populations. Future research is needed to improve practice efficiency and to inform regulatory guidelines for web-based care.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".