Pretreatment motivation and therapy outcomes in eating disorders: A systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Objective Identifying modifiable predictors of outcomes following treatment for eating disorders may help to tailor interventions to patients' individual needs, improve treatment efficacy, and develop new interventions. The goal of this meta‐analysis was to quantify the association between pretreatment motivation and posttreatment changes in eating disorder symptomology. Method We reviewed 196 longitudinal studies reporting on change on indices of overall eating‐disorder symptomatology, weight gain, binge‐eating, vomiting, anxiety/depression, and treatment adherence. Meta‐analyses were performed using two complementary approaches: (a) combined probability analysis using the added Z's method; (b) effect size analyses. Using random‐effect models, effect sizes were pooled when there were at least three studies with the same type of statistical design and reporting statistics on the same outcome. Heterogeneity in study outcome was evaluated using Q and I2 statistics. Studies were reviewed qualitatively when the number of studies or reported data were insufficient to perform a meta‐analysis. Results Forty‐two articles were included. Although samples and treatments differed substantially across studies, results across studies were remarkably consistent. Both combined‐probability and effect‐size analyses indicated positive effects of pretreatment motivation on improvement in general eating‐disorder symptoms (Cohen's r = .17), and an absence of effects on anxiety/depression symptoms. Remaining outcome indices were subject to selective reporting and/or small sample size bias. Discussion Our findings underscore the importance of incorporating treatment engagement approaches in the treatment of eating disorders. Optimal reporting of study findings and improving study quality would improve future efforts to obtain an in‐depth understanding of the relationship between motivation and eating disorder symptoms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.042 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".