Multidisciplinary implementation of family-based treatment delivered by videoconferencing (FBT-V) for adolescent anorexia nervosa during the COVID-19 pandemic
Bibliographic record
Abstract
Family-Based Treatment (FBT)-the most widely supported treatment for pediatric eating disorders-transitioned to virtual delivery in many programs due to COVID-19. Using a blended implementation approach, we systematically examined therapist adherence to key components of FBT and fidelity to FBT by videoconferencing (FBT-V), preliminary patient outcomes, and team experiences with our FBT-V implementation approach as well as familial perceptions of FBT-V effectiveness. We examined our implementation approach across four pediatric eating disorder programs in Ontario, Canada, using mixed methods. Participants included therapists (n = 8), medical practitioners (n = 4), administrators (n = 6), and families (n = 5; 21 family members in total). We developed implementation teams at each site, provided FBT-V training, and offered clinical and implementation consultation. Therapists submitted video recordings of their first four FBT-V sessions for fidelity rating, and patient outcomes. Therapists self-reported readiness, attitudes, confidence, and adherence to FBT-V. Focus groups were conducted with each team and family after the first four sessions of FBT-V. Quantitative data were analyzed using repeated measures ANOVA. Qualitative data were analyzed using directed and summative content analysis. Therapists adhered to key FBT components and maintained FBT-V fidelity. Changes in therapists' readiness, attitudes, and confidence in FBT-V over time were not significant. All patients gained weight. Focus groups revealed implementation facilitators/barriers, positives/negatives surrounding FBT-V training and consultation, suggestions for improvement, and effectiveness attributed to FBT-V. Our implementation approach appeared to be feasible and acceptable. Future research with a larger sample is required, furthering our understanding of this approach and exploring how organizational factors influence treatment fidelity.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".