The hybrid space in eating disorder treatment: towards a personalized approach to integrating telehealth and in-person care
Bibliographic record
Abstract
The combination of in-person and telehealth treatment for individuals with eating disorders is becoming an important clinical and research avenue. Despite this, a framework for describing such care, which is coming to be known as hybrid treatment, is lacking. We propose a definition for "the hybrid space" and a conceptual model that delineates the characteristics of hybrid interventions, using a person-centered approach. These characteristics include sociodemographic characteristics and social determinants of health; factors determining use; clinical characteristics; treatment context, participants, and services provided; treatment modality; and the proportion of in-person to telehealth care. Such a model may be helpful in steering development in this nascent field as it provides a framework that clinicians can flexibly adapt to their specific contexts and that researchers can investigate more rigorously. This model may contribute to the improvement of eating disorder treatment as hybrid interventions have the potential to exploit the best of both in-person and telehealth care while offering the possibility for personalizing and tailoring treatment to individuals. Ultimately, we hope that this framework will be a useful clinical tool which can lead to the development of guidelines for clinical practice.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.013 | 0.017 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".