A longitudinal evaluation of a biopsychosocial model predicting BMI and disordered eating among young adults
Bibliographic record
Abstract
Objective This study examined the utility of a biopsychosocial model to explain both higher body mass index (BMI) and disordered eating. The study was designed to examine the predictors of higher BMI and a number of measures of disordered eating (dietary restraint, drive for muscularity, drive for thinness, binge eating, and compensatory behaviour).Method Young adults (N = 838) recruited from seven countries, grouped into four regions (Europe, North American countries, Australia, Japan), completed an online survey, with each completion being 12 months apart. The survey included assessments of BMI and disordered eating, and a range of biological, psychological and sociocultural factors expected to predict both outcomes.Results Results revealed unique patterns of association between predictors and BMI as well as different measures of disordered eating in the four geographical regions.Conclusions The findings identify the specific nature of biopsychosocial factors that predict both higher BMI and different aspects of disordered eating. They also demonstrate that caution needs to be exercised in generalising findings from one country to other countries.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".