Decisional balance in anorexia nervosa: capitalizing on ambivalence
Bibliographic record
Abstract
Abstract Previous research has shown that unlike two‐factor solutions found in other populations, the Decisional Balance (DB) scale for anorexia nervosa consists of three factors: Benefits (Pros), Burdens (Cons), and Functional Avoidance. Objective The purpose of this study was to examine the psychometric properties of the DB scale for anorexia nervosa and to investigate relationships between DB scores and pre‐action stages of change. Method Eighty women with anorexia nervosa were recruited from consecutive referrals to an eating disorder clinic. Participants completed the DB scale, as well as measures to assess validity. Results The DB scale demonstrated good convergent and discriminant validity. Furthermore, relative to individuals in precontemplation, individuals in contemplation reported more disadvantages of anorexia nervosa (i.e. higher Burdens) and more insight regarding how anorexia nervosa provides a means to avoid (i.e. higher Functional Avoidance). Benefits of anorexia nervosa did not change between pre‐action stages. Discussion The clinical implications for enhancing readiness to change are addressed. 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".