Understanding progress and challenges in women's health and wellbeing in exemplar countries: A time-series study identifying positive outliers
Bibliographic record
Abstract
Background: Women's health and wellbeing (WHW) forms a multidimensional continuum across the life course, with intersecting power dynamics including socioeconomic and ethnic positioning. The WHW Exemplars project uses robust quantitative approaches to identify best-performing low-income and middle-income countries (LMICs) in improving WHW across the entire life course.Methods: Using the life course approach, we created a list of 32 cross-sectional indicators belonging to nine dimensions based on a conceptual framework to assess progress in WHW. Small population countries (people), high-income countries, and those among the top ten in the fragility index were excluded. We calculated the average annual rates of change (AARC; 2000-19) for each indicator, which were then standardised around the regional mean for comparability. The standardised values were aggregated into a final score for each country (the lower the score, the worse the country's performance). We assessed the performance of countries from a regression of the aggregated scores and the AARC of gross domestic product. We evaluated the performance of each country relative to regional peers across life course stages and dimensions based on data availability and improved performance.Findings: The final standardised score ranged from -11·0 in Costa Rica to 14·8 in Cambodia (measure of dispersion of the standarised score: 1·0 [SD 5·9]). Cambodia, Ethiopia, Peru, and Türkiye ranked highest in the life course and dimension assessment in their regions. After triangulation of results, the best-performing countries in WHW were Bangladesh, Cambodia, and India in south Asia and east Asia and the Pacific; Congo (Brazzaville), Ethiopia, Rwanda, and Sierra Leone in sub-Saharan Africa; Peru, Bolivia, and Colombia in Latin America and the Caribbean; and Morocco, Türkiye, Kazakhstan, and Azerbaijan in Europe and central Asia and the Middle East and north Africa.Interpretation: This study quantified the performance of WHW across the life course among LMICs over the past two decades and identified good performers that might be selected as exemplars. The study also highlights low data availability and quality relating to this topic
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".