Flexible or rigid control of eating scale: development and validation of the FORCES in women
Bibliographic record
Abstract
BACKGROUND: Many dieters show a pattern of disinhibited eating following a diet violation, and it has been proposed that the nature of their dietary restraint (i.e., whether they are rigid or flexible in their pursuit of dietary control) could prove beneficial in explaining variability in the occurrence of disinhibited eating. However, existing measures of rigid and flexible control do not adequately separate these two styles of dietary restraint. METHOD: The current studies aim to develop a new scale that more clearly differentiates the constructs of rigid and flexible control of eating. Exploratory and confirmatory factor analysis were used to develop and validate the new scale across three distinct samples of women (total N = 1048). RESULTS: Factor analysis identified five total factors: three relating to the rigid control of food intake (Strict Behaviours, Negative Emotions, and Worry), and two relating to the flexible control of food intake (Flexible Beliefs and Positive Emotions). The Flexible or Rigid Control of Eating Scale (FORCES) had good internal consistency, a reliable factor structure that replicated across the three samples of women and provided a clear separation between rigid and flexible control. CONCLUSIONS: The FORCES may allow researchers to explain why some dieters are more likely to engage in disinhibited eating than are others and can be a beneficial step toward addressing the negative consequences of maladaptive dieting behaviour.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.000 |
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".