Associations between area- and individual-level community belonging and self-rated health
Bibliographic record
Abstract
Abstract Background Previous studies point to the importance of individuals’ sense of community belonging to multiple measures of health and well-being. However, the extent to which collective sense of belonging within neighbourhoods might influence individual health has not been characterized. The objectives of this study are to describe variations in self-rated health by a novel area-level measure of community belonging and determine the impact of including these measures in models of individual-level community belonging and self-rated health. Methods We conducted a cross-sectional study of respondents of the 2020 Canadian Community Health Survey (CCHS) living in census tracts. These data were merged with novel small area estimates of community belonging derived by Statistics Canada from the CCHS 2016-2019. Multinomial logistic regression models were used to analyse associations of individual- and area-level community belonging, and self-rated health. We adjusted for sex, age, recent immigrant status, visible minority status, province, marital status, presence of children in the household, smoking status, presence of chronic conditions, income, and employment status. All results were generated using survey weights and bootstraps representing a subpopulation of 21 million people. Results A greater proportion of CCHS respondents living in neighbourhoods with the strongest collective sense of community belonging reported being in good, very good, or excellent health. Models indicate that living in a neighbourhood with low collective sense of community belonging is associated with higher odds of reporting being in poor or fair health (OR = 1.44, 95% CI 1.15-1.81) even after adjusting for other factors, including individual-level sense of community belonging, which also remained independently associated with self-rated health. Conclusions Both area- and individual-level sense of community belonging are independently associated with self-rated health. Key messages • The collective sense of belonging within neighbourhoods may influence health outcomes. • Measures of area-level and individual-level community belonging may capture different aspects of social connections with respect to health.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".