Examining the association between work stress, life stress and obesity among working adult population in Canada: findings from a nationally representative data
Bibliographic record
Abstract
BACKGROUND: Obesity is a priority public health concern in Canada and other parts of the world. The study primarily aims at assessing the role of self-perceived work and life stress on obesity among working adults in Canada. METHODS: The study was conducted based on a total of 104,636 Canadian adults aged 18 and above, extracted from the 2017-2018 Canadian Community Health Survey (CCHS) data. We used a mixed-effect logistic regression model to determine the possible association between two stress variables and obesity, controlling for other variables in the model. The random effect term accounts for the correlation among the observations from the same health region. RESULTS: . More than two-thirds of the respondents reported that their stress level is a bit stressful to extremely stressful. The results of multivariable mixed-effect logistic regression showed that the odds of obesity were 1.432 times (95% CI: 1.248-1.644) among those who reported extremely work-related stress, compared to those who had no work-related stress. Perceived life stress was not significantly associated with obesity risk among working adult population, after adjusting other factors. CONCLUSION: The study concluded that obesity among Canadian adults is 18.7% of the working adult population being obese. Given the reported high prevalence of stress and its effect on obesity, the findings suggested improving social support systems, individual/group counseling, and health education focusing on work environments to prevent and manage stressors and drivers to make significant program impacts.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.003 | 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".