Predictors of Residential Mobility among Older Canadians and Impact on Analyses of Place and Health Relationships
Bibliographic record
Abstract
This study aimed to identify predictors of residential mobility in 55+ Canadians, to characterise neighbourhood changes following mobility, to assess whether such changes differ according to income, and to evaluate for cross-sectional estimations of place-health relationships the extent of bias associated with residential mobility. Using longitudinal data from the Canadian National Population Health Study (NPHS), residential mobility was operationalised by a change in postal code between two consecutive waves. Individuals' sociodemographic factors and neighbourhood characteristics were analysed in relation to mobility. Bias in cross-sectional estimations of place-health associations was assessed analysing neighbourhood-level deprivation and housing quality in relation to self-assessed health. Multiple age-related events were predictive of moving. Three out of 10 individuals moved at least once. Two thirds of movers experienced a change in neighbourhood type and such changes were not associated with income. No systematic biases in estimating place effects on health using cross-sectional data were observed. Given that individual-level socioeconomic status (SES) was neither a predictor of moving nor of its consequences in terms of neighbourhood type, controlling for SES could potentially lead to biased estimations of place-health associations. Results suggest that cross-sectional data can yield valid estimations of place-health associations among older adults.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".