Self-Rated Health Trajectories in the United States and the United Kingdom: A Comparative Study
Bibliographic record
Abstract
OBJECTIVES: We reviewed literature on comparative social policy and life course research and compared associations between health and socioeconomic circumstances during an 11-year period in the United States and the United Kingdom. METHODS: We obtained data from the US Panel Study of Income Dynamics and the British Household Panel Survey (1990-2002). We used latent transition analysis to examine change in self-rated health from one discrete state to another; these health trajectories were then associated with socioeconomic measures at the beginning and at the end of the study period. RESULTS: We identified good and poor latent health states, which remained relatively stable over time. When change occurred, decline rather than improvement was more likely. UK populations were in better health compared with US populations and were more likely to improve over time. Labor market participation was more strongly associated with good health in the United Kingdom than in the United States. CONCLUSIONS: National policies and practices may be keeping more US workers than UK workers who are in poor health employed, but British policies may give UK workers the chance to return to better health and to the labor force.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".