Self-rated health: caught in the crossfire of the quest for 'true' health?
Bibliographic record
Abstract
This issue of the International Journal of Epidemiology features three papers that assess the predictive power of self-rated health (SRH) for mortality between various socio-economic groups, respectively within three developed countries (France, The Netherlands and the United States).1–3 The studies published in this issue follow in the footsteps of Burstrom and Fredlund4 and Van Doorslaer and Gerdtham5 who found that the association of SRH with mortality does not differ by either occupational group or income in Sweden. However, the similarities end there, as all the studies presented here found some evidence for a modifying effect of socio-economic status (SES) on the relationship between SRH and mortality, albeit with gradients in sometimes opposite directions. Mixed findings such as these could be—misleadingly—taken as evidence that SRH is not a reliable measure of health for the study of social inequalities in health; instead, in this editorial, I will show how these differences provide fruitful insights for understanding how individuals assess their own health and thus point the way towards a need for more research designed to test these specific hypotheses.
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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 | 0.013 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.040 | 0.061 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".