Health and Residential Mobility in Later Life: A New Analytical Technique to Address an Old Problem
Bibliographic record
Abstract
For some time researchers have known that the relationship between health and the residential mobility of the elderly is not straight forward and changes with age. Attempts to examine this relationship in multi-variate models using cross-sectional data have resulted in contradictory or ambiguous findings. One solution has been to create separate models for different age groups. However, the onset of poor health differs considerably by individual, particularly for the "young-old". Multi-variate proportional hazards models using longitudinal data offer a new approach to address this problem. As an example, data from the Ontario Longitudinal Study of Aging have been analyzed using proportional hazards models as compared with logistic regressions. The logistic regressions yield typically ambiguous results. The proportional hazards models indicate a reversal with time in the relationship between one of the two mid-life health measures and residential mobility, and the results for both measures are consistent with the theoretical literature.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".