A national cohort study of community belonging and its influence on premature mortality
Bibliographic record
Abstract
BACKGROUND: Community belonging, an important constituent of subjective well-being, is an important target for improving population health. Ageing involves transitioning across different social conditions thus, community belonging on health may vary across the life course. Using a nationally representative cohort, this study estimates the life stage-specific impact of community belonging on premature mortality. METHODS: Six cycles of the Canadian Community Health Survey (2000-2012) were combined and linked to the Canadian Vital Statistics Database (2000-2017). Respondents were followed for up to 5 years. Multivariable-adjusted modified Poisson regression models were used to estimate the relative risk of premature mortality for three life stages: early adulthood (18-35 years), middle adulthood (36-55 years) and late adulthood (56-70 years). RESULTS: The final analytical sample included 477 100 respondents. Most reported a 'somewhat strong' sense of belonging (45.9%). Compared with their 'somewhat strong' counterparts, young adults reporting a 'somewhat weak' sense of belonging exhibited an increased relative risk (RR) of 1.76 (95% CI 1.27 to 2.43) for premature mortality, whereas middle-aged adults reporting the same exhibited a decreased RR of 0.82 (95% CI 0.69, 0.98). Among older adults, groups reporting a 'very strong' (RR 1.10, 95% CI 1.01, 1.21) or a 'very weak' sense (RR 1.14, 95% CI 1.01, 1.28) of belonging exhibited higher RRs for premature mortality. CONCLUSION: The results demonstrate how community belonging relates to premature mortality differs across age groups underscoring the importance of considering life stage-specific perspectives when researching and developing approaches to strengthen belonging.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".