The Research on Eating and Adolescent Lifestyle (REAL) Study Protocol: A Shared Biopsychosocial Model for Predicting Eating Disorders and Obesity in Adolescents
Bibliographic record
Abstract
This manuscript describes the rationale, design and methods of the Research on Eating and Adolescent Lifestyles Study (REAL), which from 2006-2013, aimed to test an integrative biopsychosocial model of cross-sectional and longitudinal predictors and developmental mechanisms for both eating disorders (EDs) and obesity in a large sample of Canadian youth. The present study examined predictors from four main areas of risk: biological (e.g. weight status, pubertal stage), environmental (e.g. societal influences), individual (e.g. self-perception), and eating and weight specific behaviors (e.g. disordered eating) to test direct and indirect paths for both ED and obesity simultaneously as outcomes. Participants were recruited based on a convenience sample from 43 urban and rural public schools, and data collection ran from 2006-2013. The cross sectional sample consisted of 3043 males and females in grades 7-12. The longitudinal sample was comprised of 1197 students in grades 7 and 9 who completed annual follow ups for 7 years. The REAL study is unique as it proposes a biopsychosocial model to explain EDs and obesity in adolescents under parallel processes within a single model, and is more integrative and comprehensive than previous models. Findings from this study were intended to inform prevention initiatives and further the understanding of the etiological similarities amongst EDs and obesity in a large sample of both male and female youth across adolescence.
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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 | 0.029 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".