The Efficacy of Motivational Interviewing and Adapted Motivational Interviewing as an Eating Disorder Intervention: A Scoping Review
Bibliographic record
Abstract
In any given year, approximately 25 million Canadians aged 15 years and older are at risk for developing an eating disorder (ED). Eating disorders (EDs) have the highest mortality rate among all mental disorders and patients have been found to be ambivalent to change. Thus, recent attention has been given to interventions focusing on enhancing patients’ readiness to change. To that end, the two-fold purpose of this scoping review was to: (1) ascertain if MI and/or adapted MI improved ED patients’ readiness to change, and (2) examine the effectiveness of MI and adapted MI on improving eating pathology. Ten studies were included in this review based on the inclusion criteria. Seven studies reported MI and adapted MI increased readiness to change from pre- to post-treatment and/or follow-up. Of these studies, five only found increases in readiness to change in the treatment group. In opposition to the other findings, Treasure et al. reported a significant increase in readiness to change in both groups, with this increase being greater in the comparison group. Three studies found non-significant changes in readiness to change; Weiss et al. reported no changes in either treatment or control group while two other studies found non-significant changes in readiness to change in both groups. Accordingly, there is potential for MI to be a successful intervention for ED patients’; however, more research is needed to develop a stronger evidence base.
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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 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".