Decision‐making processes for the uptake and implementation of family‐based therapy by eating disorder treatment teams: A qualitative study
Bibliographic record
Abstract
OBJECTIVE: To explore the decision-making processes involved in the uptake and implementation of evidence-based treatments (EBTs), namely, family-based treatment (FBT), among therapists and their administrators within publically funded eating disorder treatment programs in Ontario, Canada. METHOD: Fundamental qualitative description guided sampling, data collection, and analytic decisions. Forty therapists and 11 administrators belonging to a network of clinicians treating eating disorders completed an in-depth interview regarding the decision-making processes involved in EBT uptake and implementation within their organizations. Content analysis and the constant comparative technique were used to analyze interview transcripts, with 20% of the data independently double-coded by a second coder. RESULTS: Therapists and their administrators identified the importance of an inclusive change culture in evidence-based practice (EBP) decision-making. Each group indicated reluctance to make EBP decisions in isolation from the other. Additionally, participants identified seven stages of decision-making involved in EBT adoption, beginning with exposure to the EBT model and ending with evaluating the impact of the EBT on patient outcomes. Support for a stage-based decision-making process was in participants' indication that the stages were needed to demonstrate that they considered the costs and benefits of making a practice change. Participants indicated that EBTs endorsed by the Provincial Network for Eating Disorders or the Academy for Eating Disorders would more likely be adopted. DISCUSSION: Future work should focus on integrating the important decision-making processes identified in this study with known implementation models to increase the use of low-cost and effective treatments, such as FBT, within eating disorder treatment programs.
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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 | 0.026 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".