A Readiness Ruler for Assessing Motivation to Change in People with Eating Disorders
Bibliographic record
Abstract
OBJECTIVE: We examined the psychometric properties of the Eating Disorder Readiness Ruler a simple self-report instrument designed to enable rapid assessment of readiness to change problematic eating behaviours in people with clinical eating disorders. METHOD: We administered the ED-RR, the Eating Disorders Examination Questionnaire and a measure of autonomous and controlled motivation for change to 206 individuals receiving outpatient treatment for an eating disorder. RESULTS: A principal axis factoring analysis of the ED-RR yielded a significant two-factor solution (explaining 59% of variance)-one factor pertaining to restriction and body image preoccupation (four items), the other to binge-eating and vomiting symptoms (two items). The ED-RR showed good internal consistency (alpha coefficients for the two factors being .77 and .84 respectively). Furthermore, individuals reporting higher readiness showed higher scores on independent measures of autonomous motivation and greater symptom reductions over time. DISCUSSION: Results suggest that the ED-RR is a psychometrically sound tool with potential clinical utility. Copyright © 2017 John Wiley & Sons, Ltd and Eating Disorders Association.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".