Implementing highly specialized and evidence-based pediatric eating disorder treatment: protocol for a mixed methods evaluation
Bibliographic record
Abstract
BACKGROUND: Eating disorders, which include anorexia nervosa and bulimia nervosa, are common in adolescent females and can have serious emotional and physical consequences, including death. Despite our knowledge about the severity of these illnesses, previous research indicates that adolescent patients are not receiving the best available treatment with fidelity. The main goal of this project is to reduce the knowledge gap between what research indicates is the best known treatment and what is actually delivered in clinical practice. Informed by the National Implementation Research Network model and the Consolidated Framework for Implementation Research meta-theory, our primary study aim is to increase the capacity of Ontario-based therapists to provide family-based treatment, by providing training and ongoing supervision. METHODS/DESIGN: We will use a multi-site case study with a mixed method pre/post design to examine several implementation outcomes across four eating disorder treatment programs. We will provide a training workshop on family-based treatment as well as ongoing monthly supervision. In addition, we will assemble implementation teams at each site and coach them by phone on a monthly basis regarding any process issues. Our main outcomes include fidelity to the treatment model using quantitative evaluation of audio-recorded therapy sessions, as well as qualitative analysis of the perceptions of the implementation process using audio-recorded focus groups with all clinicians and administrators involved in the study. DISCUSSION: To our knowledge, this is the first study to evaluate an implementation strategy for an evidence-based treatment for eating disorders. Challenges to date include obtaining ethics approval at all sites, and recruitment. This research will help to inform future studies on how to best implement evidence-based treatments in this field.
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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 | 0.119 | 0.092 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.066 | 0.013 |
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".