Introduction to the special issue collection, How and why are health inequalities changing over time?
Bibliographic record
Abstract
•Health inequalities remain persistent and durable, yet how and why they change over time is less known and examined.•This Special Issue brings together 16 studies examining trends in health inequalities across 20+ countries, including many outside the Global North.•While overall health outcomes improved over time in many studies, inequalities often persisted.•As such, concerted efforts are required to reduce health inequalities specifically, by addressing inequalities in the underlying macrosocial drivers of health.
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.
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 | 0.024 | 0.082 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.013 | 0.025 |
| Insufficient payload (model declined to judge) | 0.037 | 0.023 |
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".