The validity of Dietary Restraint Scales: Comment on Stice et al. (2004).
Bibliographic record
Abstract
In 4 empirical studies, E. Stice, M. Fisher, and M. R. Lowe calculated the correlations between some widely used dietary restraint scales and food intake. Failing to find substantial negative correlations, they concluded that these scales were invalid. The current article challenges this conclusion. For one thing, there is some evidence that restrained eaters do eat less than do unrestrained eaters under controlled experimental conditions favoring self-control. Dietary restraint is also associated with tendencies toward disinhibition under conditions favoring loss of self-control; such disinhibition often masks (but does not invalidate) the construct of dietary restraint. For these and other reasons, the assessment of food intake at a single eating episode may not capture overall dietary restriction. Finally, how much one eats does not necessarily indicate whether one has eaten less than one desired to eat. The authors suggest that the existing restraint scales do in fact validly assess restriction of food intake, albeit in a more complex fashion than is evident from simple correlations in single episodes.
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 | 0.010 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.043 | 0.035 |
| Insufficient payload (model declined to judge) | 0.004 | 0.007 |
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".