<scp>Cognitive‐behavioral</scp> therapy in the time of coronavirus: Clinician tips for working with eating disorders via telehealth when face‐to‐face meetings are not possible
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Bibliographic record
Abstract
OBJECTIVE: The coronavirus pandemic has led to a dramatically different way of working for many therapists working with eating disorders, where telehealth has suddenly become the norm. However, many clinicians feel ill equipped to deliver therapy via telehealth, while adhering to evidence-based interventions. This article draws together clinician experiences of the issues that should be attended to, and how to address them within a telehealth framework. METHOD: Seventy clinical colleagues of the authors were emailed and invited to share their concerns online about how to deliver cognitive-behavioral therapy for eating disorders (CBT-ED) via telehealth, and how to adapt clinical practice to deal with the problems that they and others had encountered. After 96 hr, all the suggestions that had been shared by 22 clinicians were collated to provide timely advice for other clinicians. RESULTS: A range of themes emerged from the online discussion. A large proportion were general clinical and practical domains (patient and therapist concerns about telehealth; technical issues in implementing telehealth; changes in the environment), but there were also specific considerations and clinical recommendations about the delivery of CBT-ED methods. DISCUSSION: Through interaction and sharing of ideas, clinicians across the world produced a substantial number of recommendations about how to use telehealth to work with people with eating disorders while remaining on track with evidence-based practice. These are shared to assist clinicians over the period of changed practice.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it