Socioeconomic patterning of self-rated health trajectories in Canada: A mixture latent Markov model
Bibliographic record
Abstract
This thesis investigates the association between socioeconomic position and self-rated health trajectories among Canadians. Data come from the Survey of Labour and Income Dynamics (SLID), Panel 4 (year 2002 to 2008), conducted by Statistics Canada. These longitudinal data are analyzed using mixed latent Markov model which allows for modeling multiple trajectories of health. Goodness of fit tests showed three trajectories (good health, poor health, and fluctuating health) to provide the best fit to the data. The results show that more than three quarters of Canadians were in the constant good health trajectory whereas 13.95% and 7.99% of Canadians were respectively in the persistent ill health trajectory and fluctuating health trajectory. The relative risk ratios indicate that increasing income and education are independently associated with a greater likelihood of belonging to the persistent good health trajectory rather than the persistent poor health trajectory. Both associations accounted for possible confounders including gender, age, marital status, immigrant status and visible minority status. These results suggest that a socioeconomic gradient exists in the likelihood of belonging to given health trajectories. In addition, the use of mixed latent Markov model is robust in accounting for certain issues inherent to longitudinal analysis. Notably, the Markov chain models the dependency between repeated measurements within the same individual; it allows for the modeling of the latent variables estimate measurement error; the heterogeneity of the population is accounted by finite mixture modeling; and lastly, missing data are dealt with using full information maximum likelihood.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".