The development of a decisional balance scale for anorexia nervosa
Bibliographic record
Abstract
Abstract The purpose of this study was to develop a Decisional Balance (DB) measure of readiness for change in anorexia nervosa. A total of 246 women with anorexia nervosa completed a 60‐item DB scale. A 3‐factor solution provided the best fit for the DB data, which reduced the scale to 30 items. Two of these, namely Burdens and Benefits, resembled the pro and con factors found in previous research. The third factor, Functional Avoidance, was unique to the decisional balance literature; it included items reflecting the ways that anorexia nervosa provides a way to avoid dealing with aversive emotions, challenges, and responsibilities. The DB demonstrated good internal consistency and acceptable test–retest reliability. This measure could be used to help us better understand the shifts that occur as individuals with anorexia nervosa develop a desire to change. Copyright © 2002 John Wiley & Sons, Ltd and Eating Disorders Association.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".