Clinician adherence to guidelines in the delivery of family‐based therapy for eating disorders
Bibliographic record
Abstract
OBJECTIVE: Clinicians have been shown to drift away from protocol in their delivery of evidence-based treatments. This study explores this phenomenon in the delivery of family-based therapy (FBT) for eating disorders, and the clinician characteristics that might explain such therapist drift. METHOD: The participants were 117 clinicians who reported using FBT for eating disorders. They completed an online survey, which included questions relating to clinician characteristics, caseload, and reported use of FBT manuals and core therapeutic tasks, as well as a measure of anxiety. RESULTS: The use of core FBT tasks was higher than for other therapies, but there were still noteworthy gaps between recommended and reported practice. Approximately a third of clinicians reported delivering "FBT" that deviated very substantially from evidence-based protocols, often appearing to be on an individual therapy basis. Using an FBT manual to guide treatment delivery was associated with greater adherence to recommended techniques. Clinician caseload and anxiety were associated with differences in the use of specific FBT tasks. DISCUSSION: Consistent with previous research regarding clinicians' use of other therapies, the delivery of FBT for the eating disorders is not homogeneous. CONCLUSION: Further investigation of this phenomenon is needed to determine the impact of deviating from treatment protocols on the effectiveness of FBT for the eating disorders.
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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 | 0.070 | 0.233 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".