Bibliographic record
Abstract
This article considers the increasing challenge of the fair allocation of scarce public health care resources by focusing on services for women and girls. It considers different ways of thinking about fairness in health care reform, the role of courts in promoting fairness, and the use of affirmative action measures to remedy health disparities. The health of individuals and populations is shown to be affected by clinical services, the organization and functioning of health systems, and underlying socio-economic determinants. Different theories of justice are addressed that affect assessments of fairness, considering availability, accessibility, acceptability of and accountability for services. The transition in judicial dispositions is traced, from deference to governmental resource allocation decisions to evidence-based scrutiny of governmental observance of constitutional and human rights legal obligations. The appropriate use of affirmative action measures to improve equality in health status is explored, given the increasingly unacceptable disparities in health among subgroups of women within countries.
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 | 0.066 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.055 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.010 | 0.009 |
| 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".