Bibliographic record
Abstract
Diversity in both biological attributes and the external, lived environment gives rise to different susceptibilities, exposures, health outcomes, and longevity. Public policy can modify the effects of external differences, if groups at greatest risk are identified and pathways to excess vulnerability are understood, by rebalancing and redistributing the inputs or social determinants that work their way under the skin to ultimately cause biological disadvantage. In the past three decades, a large volume of research has identified the nature of these social determinants of health—including income, socioeconomic status (SES), income inequality, social connectedness, and social capital—and the pathways by which they undermine or reinforce innate health. Often listed among these, but rarely studied, is gender. Medical research may identify sex differences when they exist; however, the varied social roles, expectations, and constraints experienced by men and women in a given society go well beyond the individual and sex differences and are rarely examined as inputs responsible for variation in health outcomes. As a result, health-affirming policies tend to homogenize groups (e.g., assuming that all women are the same) or target individual behaviors, and do so in a gender-blind fashion rather than addressing structural biases and inequities that undermine those behaviors. This article explores the nature of gender as a determinant of health and describes how the effects of gender inequities can be included in health outcomes research that can then shape health planning and policy.
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 | 0.119 | 0.147 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.007 | 0.015 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".