When women win, we all win—Call for a gendered global <scp>NCD</scp> agenda
Bibliographic record
Abstract
Gender is a social determinant of health, interacting with other factors such as income, education, and housing and affects health care access and health care outcomes. This paper reviews key literature and policies on health disparities and gender disparities within health. It examines noncommunicable disease (NCD) health outcomes through a gender lens and challenges existing prevailing measures of success for NCD outcomes that focus primarily on mortality. Chronic respiratory disease, one of the four leading contributors to NCD mortality, is highlighted as a case study to demonstrate the gender gap. Women have different risk factors and higher morbidity for chronic respiratory disease compared to men but morbidity is shadowed by a penultimate research focus on mortality, which results in less attention to the gap in women's NCD outcomes. This, in turn, affects how resources, programs, and interventions are implemented. It will likely slow progress in reducing overall NCD burden if we do not address risk factors in an equitable fashion. The article closes with recommendations to address these gender gaps in NCD outcomes. At the policy level, increasing representation and inclusion in global public health leadership, prioritizing NCDs among marginalized populations by global health societies and political organizations, aligning the gendered global NCD agenda with other well-established movements will each catalyze change for gender-based disparities in global NCDs specifically. Lastly, incorporating gender-based indicators and targets in major NCD-related goals and advancing gender-based NCD research will strengthen the evidence base for women's unique NCD risks and health outcomes.
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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 | 0.020 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.017 | 0.024 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.012 | 0.024 |
| Insufficient payload (model declined to judge) | 0.033 | 0.008 |
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".