Obesity and adverse childhood experiences in relation to stress during the COVID-19 pandemic: an analysis of the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
BACKGROUND: People with obesity are at increased risk of chronic stress, and this may have been exacerbated during the COVID-19 pandemic. Adverse childhood experiences (ACE) are also associated with both obesity and stress, and may modify risk of stress among people with obesity. The objectives of this study were to evaluate the associations between obesity, ACEs, and stress during the pandemic, and to determine if the association between obesity and stress was modified by ACEs. METHODS: A longitudinal study was conducted among adults aged 50-96 years (n = 23,972) from the Canadian Longitudinal Study on Aging (CLSA) COVID-19 Study. Obesity and ACEs were collected pre-pandemic (2015-2018), and stress was measured at COVID-19 Exit Survey (Sept-Dec 2020). We used logistic, Poisson, and negative binomial regression to estimate relative risks (RRs) and 95% confidence intervals (CIs) for the associations between obesity, ACEs, and stress outcomes during the pandemic. Interaction by ACEs was evaluated on the additive and multiplicative scales. RESULTS: People with obesity were more likely to experience an increase in overall stressors (class III obesity vs. healthy weight RR = 1.19; 95% CI: 1.12-1.27) as well as increased health related stressors (class III obesity vs. healthy weight RR: 1.25; 95% CI: 1.12-1.39) but did not perceive the consequences of the pandemic as negative. ACEs were also associated an increase in overall stressors (4-8 ACEs vs. none RR = 1.38; 95% CI: 1.33-1.44) and being more likely to perceive the pandemic as negative (4-8 ACEs vs. none RR = 1.32; 95% CI: 1.19-1.47). The association between obesity and stress was not modified by ACEs. CONCLUSIONS: Increased stress during the first year of the COVID-19 pandemic was observed among people with obesity or ACEs. The long-term outcomes of stress during the pandemic need to be determined.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".