Adverse childhood experiences and perceived body size across the life course: a longitudinal study using data from the Canadian Longitudinal Study on Aging (CLSA)
Bibliographic record
Abstract
Background: Early life adversity has long-term effects; however, the influence on changes in body size across the life course is not well understood. Objectives of this study were to define trajectories of body size across the life course and to evaluate the association between adverse childhood experiences (ACEs) and perceived life course body size trajectories. Methods: A longitudinal study using data from the Canadian Longitudinal Study on Aging (CLSA) was conducted (n=11 830). Adults aged 49-93 were asked to recall eight ACEs and their perceived body size at ages 25, 45, 55, 65 and current using pictograms. Body size trajectories were identified using latent class growth mixture modelling. Multinomial logistic regression was used to estimate ORs and 95% CIs for the association between ACEs and perceived body size trajectories. Effect modification by sex was explored. Results: Six distinct life course body size trajectories were identified: consistently low (9.7%), consistently mid-size (24.7%), moderate increase (37.4%), strong increase (14.7%), decline (4.9%) and consistently high (8.6%). High ACE exposure, compared with none, was associated with increased odds of the strong increase (OR: 1.49; 95% CI: 1.21 to 1.83) and consistently high (OR: 1.36; 95% CI: 1.08 to 1.73) body size trajectories, compared with the moderate-increase trajectory. For females, there was a strong association for those who reported 4-8 ACEs with the consistently high trajectory (OR: 1.70; 95% CI: 1.24 to 2.34) but no association for males (OR: 0.99; 95% CI: 0.69 to 1.41). Discussion: Distinct patterns of body size throughout the life course exist. ACEs are associated with trajectories that are characterised by obesity incidence in both early and later life.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".