Changing BMI scores among Canadian Indigenous and non-Indigenous children, youth, and young adults: Untangling age, period, and cohort effects
Bibliographic record
Abstract
The objective of this study was to examine age, period and cohort effects on BMI among Indigenous and non-Indigenous populations, using repeated cross-sectional survey data from the CCHS (2001 to 2014). Cross-classified random-effect two-level models were used to estimate fixed effects for age and its quadratic term (Level 1), and also to estimate random effects for time periods and birth cohorts (Level 2), while controlling for the effects of Level 1 control variables: sex, model of interview and response by proxy. Overall, the results support the hypothesis that age and period effects are primarily responsible for the current obesity epidemic.L’objectif de cette étude était d’examiner les effets de l’âge, de la période et de la cohorte sur l’IMC chez les populations autochtones et non autochtones, en utilisant des données d’enquêtes transversales répétées de l’ESCC (2001 à 2014). On a utilisé des modèles à deux niveaux à effets aléatoires croisés pour estimer les effets fixes pour l’âge et son terme quadratique (niveau 1), et également estimer les effets aléatoires pour les périodes et les cohortes de naissance (niveau 2), tout en contrôlant les effets du niveau 1 Variables de contrôle: sexe, modèle d’interview et réponse par procuration. Dans l’ensemble, les résultats confirment l’hypothèse selon laquelle les effets de l’âge et de la période sont les principaux responsables de l’épidémie actuelle d’obésité.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".