The impact of sense of belonging on health: Canadian evidence
Bibliographic record
Abstract
Sense of belonging to the community is presented as a fundamental human need, with existing evidence of its correlation with health. The relationship between belongingness and health is nevertheless endogenous as better health may foster belongingness in the community. We empirically examine the causal effect of belongingness on health using data from Canada, taking advantage of the multicultural nature of the Canadian society. We use data from the Canadian Community Health Survey from 2009 to 2014 augmented with other survey and administrative data. We address the endogeneity problem with instrumental variables, and construct instruments as the difference between the individual’s and the neighbourhood’s ethnocultural and homeownership characteristics. To account for the nonlinearity of the belongingness and health variables, we estimate our instrumental variables models by two-stage residual inclusion and a Bayesian estimation procedure. Our findings indicate that higher belongingness improves health, both for subjective measures like self-assessed health and objective measures like chronic conditions. While all age groups are positively affected, the effects are stronger for older adults, as well as for women. Health behaviours and healthcare utilization are potential mechanisms by which belongingness appears to affect health, in particular reduced smoking, and better access to a regular provider of care.
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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 | 0.011 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".