Experiences of a virtual day program for adolescents with eating disorders: a qualitative analysis of benefits and barriers
Bibliographic record
Abstract
Abstract Background Throughout the COVID-19 pandemic, there was a detrimental impact to the symptoms and treatment of eating disorders, causing an increase in medical admissions and visits. Day treatment programs (DTPs), often used to bridge the gap between inpatient and outpatient treatment, were converted to online formats. This study aims to explore the impact of the transition to virtual DTPs on eating disorder treatment from the perspective of adolescents, their caregivers, and program staff. Methods Twelve participants (3 adolescents, 4 caregivers, 5 healthcare providers) in a virtual day treatment program were interviewed using a semi-structured interview guide. Interviews were transcribed and managed with qualitative software NVivo 11.0. Conventional analysis was used to inductively identify pertinent themes related to patient, caregiver, and healthcare staff perceptions and experiences of the virtual day treatment. Summative content analysis provided counts of the barriers and benefits of virtual day treatment as identified by participants. Results The majority of participants (10/12) had exposure to both virtual and in person settings, most participants (11/12) felt in-person day programs would be superior to virtual programs. Common limitations of the virtual format were feelings of isolation, less support from healthcare providers, parental burnout, and increased disordered eating. Common benefits were increased accessibility, parental involvement, improved communication with healthcare staff, and the ability for participants to be in their home environment and eat home food. Suggestions for improvement included designing a hybrid model of day treatment, increased family involvement, extending the day program to include dinners with family, and screening for patient appropriateness for the virtual setting. Conclusion This qualitative study suggests that there are many barriers to effective implementation of virtual day programs. However, the virtual DTP program offers increased accessibility to patients during a period of a health pandemic and to patients in rural/remote areas with limited treatment options. Suggestions provided by participants in this study, such as increased family involvement, frequency of in person check-ins and increased number of meals supported by the program, may help to improve outcomes in virtual day treatment programs.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| 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".