A preliminary study of motivational interviewing as a prelude to intensive treatment for an eating disorder
Bibliographic record
Abstract
BACKGROUND: Engaging patients with an eating disorder in change is difficult and intensive treatment programs have high drop-out rates. The purpose of the study was to determine whether Motivational Interviewing (MI) in the form of a brief, pre-treatment intervention would be associated with higher completion rates in subsequent intensive treatment for an eating disorder. Thirty-two participants diagnosed with an eating disorder participated in the study. All participants were on the waitlist for admission to an intensive, hospital-based treatment program. Sixteen participants were randomly assigned to four individual sessions of MI that began prior to entrance into the treatment program (MI condition) and 16 participants were assigned to treatment as usual (control condition). The main outcome was completion of the intensive treatment program. Participants also completed self-report measures of motivation to change. RESULTS: Participants in the MI condition were significantly more likely to complete intensive treatment (69% completion rate) than were those in the control condition (31%). CONCLUSIONS: MI can be a useful intervention to engage individuals with severe eating disorders prior to participation in intensive treatment. MI as a brief prelude to hospital-based treatment for an eating disorder may help to improve completion rates in such programs. Further research is required to determine the precise therapeutic mechanisms of change in MI.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".