Monitoring Gender Equity in Health Using Gender-Sensitive Indicators: A Cross-National Study
Bibliographic record
Abstract
BACKGROUND: As gender is known to be a major determinant of health, monitoring gender equity in health systems remains a vital public health priority. Focusing on a low-income (Peru), middle-income (Colombia), and high-income (Canada) country in the Americas, this study aimed to (1) identify and select gender-sensitive health indicators and (2) assess the feasibility of measuring and comparing gender-sensitive health indicators among countries. METHODS: Gender-sensitive health indicators were selected by a multidisciplinary group of experts from each country. The most recent gender-sensitive health measures corresponding to selected indicators were identified through electronic databases (CINAHL, PsycINFO, MEDLINE, Embase, LILACS, LIPECS, Latindex, and BIREME) and expert consultation. Data from population-based studies were analyzed when indicator information was unavailable from reports. RESULTS: Twelve of the 17 selected gender-sensitive health indicators were feasible to measure in at least two countries, and 9 of these were comparable among all countries. Indicators that were available were not stratified or adjusted by age, education, marital status, or wealth. The largest between-country difference was maternal mortality, and the largest gender inequity was mortality from homicides. CONCLUSIONS: This study shows that gender inequities in health exist in all countries, regardless of income level. Economic development seemed to confer advantages in the availability of such indicators; however, this finding was not consistent and needs to be further explored. Future initiatives should include identifying health system factors and risk factors associated with disparities as well as assessing the cost-effectiveness of including the routine monitoring of gender inequities in health.
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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 | 0.019 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".