Commentary: What explains widening geographic differences in life expectancy in New Zealand?
Bibliographic record
Abstract
The ongoing collection, analysis, and dissemination of routine health statistics is essential for monitoring the health of populations.1 While the main function of such routine data analysis is to provide surveillance for particular health conditions, it may also serve to generate and support hypotheses about the causes of changes in population health. In this issue, Pearce and Dorling2 analyse routine data to show that differences in life expectancy between New Zealand's 21 health districts have widened from 1980 to 2000. More specifically, they show that life expectancy increased for all areas, but the areas that were least deprived in 2001 saw larger life expectancy gains since 1980 than did the most-deprived areas. Similar results have also been seen for the UK3 and Canada,4 but as the authors note, there has been less attention paid to investigating geographic inequalities in health than to socioeconomic or race–ethnic inequalities in health. So what might account for the widening gap in life expectancy observed among these areas in New Zealand?
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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 | 0.011 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.009 | 0.003 |
| Research integrity | 0.038 | 0.027 |
| Insufficient payload (model declined to judge) | 0.017 | 0.008 |
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".