A Multiple-Process Latent Transition Model of Poverty and Health
Bibliographic record
Abstract
Health researchers often use the life-course perspective, exploring how long-range experiences in one life domain may influence, and be influenced by, those in another. We develop a multiple-process latent transition model (MPLTM) to estimate changes in health and poverty dynamics simultaneously, using repeated measures of self-rated health and income for working-aged adults from the British Household Panel Survey. We apply the model to quantify concurrent and longitudinal effects to assess whether changes in these two processes are related or independent. Model extensions add time-invariant (cohort, gender) and time-varying (weeks nonemployed in previous year) covariates. We find both concurrent and bidirectional longitudinal relationships between poverty and health, with nonemployment appearing to mediate longitudinal health-to-poverty effects and confound longitudinal poverty-to-health effects. The MPLTM can provide quantitative estimates of complex interlocking processes that are often difficult to measure and assess.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".