Bibliographic record
Abstract
BACKGROUND: The processing of schema-related information is important in the maintenance of specific eating disorder (ED)-related belief systems and psychopathology. To date, most research on differences in the processing of ED schematic information has used interview or self-report questionnaire measures. Dieting is a known risk factor for EDs and dieters have been included in some studies. However, they have not been compared with non-dieters on a novel, objective measure of ED related schema processing. METHODS: The current study recruited healthy female volunteers from the community and divided them into dieting (n = 25) and non-dieting (n = 24) groups using rigorous criteria. ED self-schemas with content unrelated to eating, weight and shape were measured using a self-schema processing task. RESULTS: Dieters endorsed significantly more ED relevant words compared to non-dieters, whereas non-dieters rejected significantly more ED relevant words compared to dieters. Reaction times to endorsements and rejections were non-significant when the two groups were compared. In a surprise recall task, dieters recalled significantly more ED relevant words. CONCLUSION: The results of this study support the presence of ED self-schemas with negative content unrelated to eating, weight and shape in otherwise healthy dieters. Implications for future research and the early identification of individuals vulnerable to EDs are discussed.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".