The predictive validity of the DEBQ‐external eating scale for eating in response to food commercials while watching television
Bibliographic record
Abstract
OBJECTIVE: To challenge the conclusion by Jansen et al., Int J Eat Disord 2011; 44:164-168, that the widely used Dutch Eating Behavior Questionnaire (DEBQ) External Eating subscale (DEBQ-EX) lacks validity for external eating, because of limitations of that study. METHOD: In a seminaturalistic setting we measured participants' intake of crisps and M&Ms while they watched food commercials or neutral commercials spliced into a film. To avoid misclassification due to the use of median splits we used extreme scores on the DEBQ-ex (n = 60) in addition to the full range of scores (n = 125). RESULTS: As was expected, high external eaters in the food commercial condition ate more crisps than did high external eaters in the neutral commercial condition, whereas low external eaters did not eat more crisps in one condition than in the other. No such moderator effect was found for emotional eating. No significant moderator effect was found for external eating in the original sample (n = 125) using the median-split procedure. DISCUSSION: The DEBQ scale for external eating has validity and specificity for external eating provided that the participants have sufficiently extreme external eating scores and a natural setting is used.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".