A Scenario-Based Dieting Self-Efficacy Scale
Bibliographic record
Abstract
The article discusses a scenario-based dieting self-efficacy scale, the DIET-SE, developed from dieter's inventory of eating temptations (DIET). The DIET-SE consists of items that describe scenarios of eating temptations for a range of dieting situations, including high-caloric food temptations. Four studies assessed the psychometric properties of the 11-item DIET-SE. Exploratory factor analysis (N = 392) and confirmatory factors analysis (N = 124) revealed three internally consistent and reliable factors representing challenges to adhere to a diet (high-caloric food temptations [HCF], social and internal factors [SIF], negative emotional events [NEE]). Convergent validity is established with other measures of dieting self-efficacy, as well as measures of eating disinhibition, susceptibility to hunger, and weight loss competency. Criterion-related validity is provided through the assessment of goal adherence, and predictive validity is established for dieters' actual food intake (N = 68). The DIET-SE represents a short, reliable, and valid scenario-based measure of dieting self-efficacy.
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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 | 0.007 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".