Canadian pediatric eating disorder programs and virtual care during the COVID-19 pandemic: a mixed-methods approach to understanding clinicians’ perspectives
Bibliographic record
Abstract
BACKGROUND: As a result of the public health measures put in place during the COVID-19 pandemic in Canada, many health services, including those for the treatment of eating disorders, were provided at a distance. This study aims to describe the adaptations made in specialized pediatric eating disorder programs in Canada and the impact of these adaptations on health professionals' experience of providing care. METHODS: A mixed-methods design was used to survey healthcare professionals working in specialized pediatric eating disorder programs about adaptations to treatment made during the pandemic and the impact of these adaptations on their experience of providing care. Data were collected between October 2021 and March 2022 using a cross-sectional survey comprising 25 questions and via semi-structured interviews. Quantitative data were summarized using descriptive statistics and qualitative data were interpreted using qualitative content analysis. RESULTS: Eighteen healthcare professionals in Canada completed the online survey, of whom six also participated in the semi-structured interviews. The cross-sectional survey confirmed that, unlike in pre-pandemic times, the majority of participants provided medical care (15/18) and mental health care (17/18) at a distance during the pandemic, with most participants using telephone (17/18) and videoconferencing (17/18). Most (16/18) health professionals indicated that virtual care would continue to be used as a tool in pediatric ED treatment after the pandemic. Participants used a combination of virtual and in-person care, with most reporting weighing patients both in clinic (16/18) and virtually (15/18). Qualitative content analysis generated five themes: (1) responding to increased demand with insufficient resources; (2) adapting to changes in care due to the COVID-19 pandemic; (3) dealing with uncertainty and apprehension; (4) virtual care as an acceptable and useful clinical tool, and (5) optimal conditions and future expectations. Most interview participants (5/6) had globally positive views of virtual care. CONCLUSIONS: Providing virtual multidisciplinary treatment for children and adolescents with eating disorders seemed feasible and acceptable to professionals during the pandemic. Moving forward, focusing on health professionals' perspectives and providing appropriate training in virtual interventions is essential given their central role in successful implementation and continued use of virtual and hybrid care models.
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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 | 0.048 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.025 | 0.009 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".