Bibliographic record
Abstract
Measuring and monitoring social differences in health is an important component of public health surveillance for at least two reasons. First, social group differences in health tell us something about the potential impacts of structural inequalities in society. Health is a crucial component of overall well-being, and differences in health between important social groups may indicate the degree to which major social institutions structure the resources and opportunities for healthy living.1 Secondly, continued monitoring of social differences in health provides an opportunity to reconcile temporal trends in health inequalities with aetiological hypotheses regarding the causes of health differences.2 Whether the social patterning of health reflects social differences in hazardous or protective exposures (including social conditions), health behaviours, proximal risk factors, medical care, or, more likely, some combination of such factors has important implications for designing interventions to address health inequalities. Building on a strong foundation of previous research on health inequalities in New Zealand, in this issue Tobias and colleagues deliver a particularly good example of monitoring mortality inequalities between Māori and non-Māori populations.3 They measure inequalities on both the absolute and relative scale, consider the contributions of specific causes of death, and interpret their results in light of potential mechanisms and lag times between potential exposures and mortality. They find that the ethnic gap in mortality declined from the early 1950s until the mid-1980s, reversed course and increased from the mid-1980s to mid-1990s, and subsequently declined again. More importantly, they suggest that the recent patterning of widening and narrowing of inequalities is causally related to changes in socio-economic conditions resulting from the restructuring of New Zealand's economy.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.034 | 0.020 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".