The Cognitive‐Behavioural Theory and Treatment of Bulimia Nervosa: An Examination of Treatment Mechanisms and Future Directions
Bibliographic record
Abstract
Enhanced cognitive‐behavioural therapy (CBT‐E) is the current treatment of choice for bulimia nervosa. While the cognitive‐behavioural theory and treatment of bulimia nervosa have made a substantial contribution to our understanding of the disorder, approximately half of patients treated with CBT‐E fail to achieve remission of binge eating and purging. There is evidence showing that mechanisms proposed by the CBT‐E model are associated with binge eating and purging symptoms, and therefore likely important targets for treatment. To identify future directions in improving the efficacy of this treatment, and informed by a model of the client change process, we review the evidence for the hypothesised treatment mechanisms of CBT‐E. We conclude that while the proposed treatment mechanisms of CBT‐E largely change over the course of treatment, there is limited evidence that the treatment manipulations of CBT‐E are responsible for the specific changes in the proposed treatment mechanisms. In addition, given a lack of research in this area, we could find no evidence that changes in the additional treatment mechanisms outlined in CBT‐E are associated with changes in the core symptomatology of binge eating and purging. Based on these findings, we recommend that future efforts are directed towards understanding the client change process in CBT‐E and outline three clear directions for research.
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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 | 0.015 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".