The association between well-being and belonging differs across census-defined neighbourhoods for Ontario youth
Bibliographic record
Abstract
• Individual and neighbourhood differences explained variance in well-being. • Various individual, interpersonal, and neighbourhood factors predicted well-being. • Census characteristics did not predict youth well-being. • The relationship between belonging and well-being differed across neighbourhoods. • Effect modification on the belonging/well-being association was observed. The goal of the current study was to explore the association between belonging and well-being in children and youth. Close attention was paid to the role of neighbourhoods, where associations were permitted to operate differentially across youths’ local environment, in addition to individual and interpersonal factors that may account for this effect modification. Data came from a regional survey in Southern Ontario designed to monitor well-being in young people (n = 1842; aged 9-18 years). Youth provided their forward sortation area (FSA), permitting linkage with 2021 census-defined socioeconomic status (SES) and immigration levels. Two-level multilevel models revealed statistically significant variance in well-being at neighbourhood (11.1%) and individual (88.9%) levels (plus error). A random slope was observed for belonging, suggesting that the belonging/well-being association varied across neighbourhoods. Greater well-being was observed in youth born outside of Canada, and when youth reported higher family or friend support, and neighbourhood safety and resources. Effect modification (moderation) on the belonging/well-being association was observed for gender, family support, country of birth, and neighbourhood safety. Findings affirm theoretical models suggesting that well-being in young people emerges in a complex multilevel ecology. There is an ongoing need for public health programs that are sensitive to local geographic differences, while considering the role of belonging within the proximal and distal social contexts of young people.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".