Damned if you do, damned if you don't: subgroup analysis and equity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The final report from the WHO Commission on the social determinants of health recently noted: 'For policy, however important an ethical imperative, values alone are insufficient. There needs to be evidence on what can be done and what is likely to work in practice to improve health and reduce health inequities.' This is challenging, because understanding how to reduce health inequities between the poorest and better-off members of society may require a greater use of subgroup analysis to explore the differential effects of public health interventions. However, while this may produce evidence that is more policy relevant, the requisite subgroup analyses are often seen as tantamount to statistical malpractice. This paper considers some of the methodological problems with subgroup analysis, and its applicability to considerations of equity, using both clinical and public health examples. Finally, it suggests how policy needs for information on subgroups can be met while maintaining rigour.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.580 | 0.170 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.012 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 it