When women win, we all win—Call for a gendered global <scp>NCD</scp> agenda
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it