Gender Differences in Determinants and Consequences of Health and\nIllness
Bibliographic record
Abstract
This paper uses a framework developed for gender and tropical diseases\nfor the analysis of non-communicable diseases and conditions in\ndeveloping and industrialized countries. The framework illustrates that\ngender interacts with the social, economic and biological determinants\nand consequences of tropical diseases to create different health\noutcomes for males and females. Whereas the framework was previously\nlimited to developing countries where tropical infectious diseases are\nmore prevalent, the present paper demonstrates that gender has an\nimportant effect on the determinants and consequences of health and\nillness in industrialized countries as well. This paper reviews a large\nnumber of studies on the interaction between gender and the\ndeterminants and consequences of chronic diseases and shows how these\ninteractions result in different approaches to prevention, treatment,\nand coping with illness. Specific examples of chronic diseases are\ndiscussed in each section with respect to both developing and\nindustrialized countries.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".