Adapting and adopting highly specialized pediatric eating disorder treatment to virtual care: a protocol for an implementation study in the COVID-19 context
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has negatively impacted individuals with eating disorders; resulting in increased symptoms, as well as feelings of isolation and anxiety. To conform with social distancing requirements, outpatient eating disorder treatment in Canada is being delivered virtually, but a lack of direction surrounding this change creates challenges for practitioners, patients, and families. As a result, there is an urgent need to not only adapt evidence-based care, including family-based treatment (FBT), to virtual formats, but to study its implementation in eating disorder programs. We propose to study the initial adaptation and adoption of virtual family-based treatment (vFBT) with the ultimate goal of improving access to services for youth with eating disorders. METHODS: We will use a multi-site case study with a mixed method pre/post design to examine the impact of our implementation approach across four pediatric eating disorder programs. We will develop implementation teams at each site (consisting of therapists, medical practitioners, and program administrators), provide a remote training workshop on vFBT, and offer ongoing consultation during the initial implementation phase. Therapists will submit videorecordings of their first four vFBT sessions. We propose to study our implementation approach by examining (1) whether the key components of standard FBT are maintained in virtual delivery measured by therapist self-report, (2) fidelity to our vFBT model measured by expert fidelity rating of submitted videorecordings of the first four sessions of vFBT, (3) team and patient/family experiences with vFBT assessed with qualitative interviews, and (4) patient outcomes measured by weight and binge/purge frequency reported by therapists. DISCUSSION: To our knowledge, this is the first study to evaluate an implementation strategy for virtually delivered FBT for eating disorders. Challenges to date include confirming site participation and obtaining ethics approval at all locations. This research is imperative to inform the delivery of vFBT in the COVID-19 context. It also has implications for delivery in a post-pandemic era where virtual services may be preferable to patients and families living in remote locations, where access to specialized services is extremely limited. TRIAL REGISTRATION: ClinicalTrials.gov NCT04678843 , registered on December 21, 2020.
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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 | 0.118 | 0.070 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.039 | 0.009 |
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".