Is the Association Between Appearance Overvaluation and Dietary Restraint Robust to Substantive and Methodological Specifications? A Specification Curve Analysis
Bibliographic record
Abstract
In the cognitive-behavioural model, dietary restraint follows from appearance overvaluation (i.e., the core psychopathology of disordered eating).However, little research has examined the association between appearance overvaluation and dietary restraint when accounting for shared variance with other factors.Moreover, results generally hinge on researchers' decision-making, including addressing outliers and covariates.Herein, specification curve analysis was used to examine the association between appearance overvaluation and dietary restraint under 80 unique regression models based on different combinations of substantive factors and methodological decisions.Results indicated a positive association between appearance overvaluation and dietary restraint among university women (N=569; mean β=0.26), however, the association was not statistically significant when all factors were included in the model, and removal of outliers made results unstable.Hence, it is possible that sociocultural and cognitive-behavioural factors may be mediating or confounding the association.Findings also highlight the importance of limiting researcher degrees of freedom during data analysis.iii In loving memory of Meghan Reid pushed me at a time when I thought I was in over my head with my thesis.As well, I want to specially thank Mom for being my number one cheerleader and always celebrating my accomplishments, no matter how big or small.I am also extremely grateful to Dad and Cathy for their guidance, and especially indebted to Dad for his proofreading efforts.
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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 | 0.344 | 0.543 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.015 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".