Identifying health inequalities between Māori and non‐Māori using mortality tables
Bibliographic record
Abstract
Abstract While there is a need for more detailed information on health inequality to guide public health policy, the most complete and easily available data remain those in mortality tables. We investigate, via a comparative analysis of data from New Zealand on Māori and non‐Māori mortality, whether more detailed information than raw life expectancy may be extracted from the mortality tables. Given a parametric distribution for the mortality capable of fitting irregularities in mortality table data, the curvature of the survival and hazard rates can identify changes in mortality rates, such as infant and late‐life adult mortality, which allows for straightforward comparisons between the two sub‐populations. Our results identify an exogenous effect in earlier mortality among Maori, which correlates well with many published observations of health and health‐care inequalities between Māori and non‐Māori. This “proof of concept” for our method of analysis indicates that examination of bulk data such as those in mortality tables has a potential role in the design of more detailed studies involving causes of mortality.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".