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Record W4399436670 · doi:10.1080/00036846.2024.2364075

The impact of sense of belonging on health: Canadian evidence

2024· article· en· W4399436670 on OpenAlexaffabout
Ian Allan, Mehdi Ammi, F. Antoine Dedewanou

Bibliographic record

VenueApplied Economics · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsCarleton University
Fundersnot available
KeywordsEconomicsSense (electronics)Health economicsPositive economicsPublic economicsEconometricsHealth careEconomic growthEngineering

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.842
Threshold uncertainty score0.402

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.046
GPT teacher head0.370
Teacher spread0.324 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2024
Admission routes2
Has abstractyes

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