Women’s health and well-being in five birth cohorts from low- and middle-income countries: Domains and their associations with early-life conditions
Bibliographic record
Abstract
Background: Women's health and well-being (WHW) have been receiving growing attention, but limited progress has been made on how to measure its different domains in low- and middle-income countries (LMICs). We used data from five long-term birth cohorts in Brazil, Guatemala, the Philippines and South Africa to explore different domains of adult WHW, and how these domains relate to early life exposures. Methods: Based upon an a priori conceptualisation of eight postulated WHW outcomes available in the data, we grouped them as follows: human capital (intelligence quotient, schooling, height, and teenage childbearing), metabolic health (body mass index and metabolic syndrome score), and psychological (happiness and Self-Reported Questionnaire (SRQ) scores). Correlation analyses confirmed the variables theoretically belonging to the same dimension of WHW were statistically related. We then applied principal component analysis to each group of variables separately and used the first principal component as a summary quantitative measure of the corresponding WHW dimension. Finally, we assessed the association of each domain with a range of early-life factors: wealth, maternal education, maternal height, water, and sanitation, birthweight, length at two years and development quotient in mid-childhood. Results: The three domains were largely uncorrelated. Early determinants were positively associated with human capital, while birth order was negatively associated. Fewer associations were found for the metabolic or psychological components. Birthweight and weight at age two years were inversely associated with metabolic health. Maternal education was associated with better psychological health. Conclusions: Our findings indicate that WHW is multidimensional, with most women in the cohorts being compromised in one or more domains while few women scored highly in all three domains. Our analyses are limited by lack of data on adolescent exposures and on other relevant WHW dimensions such as safety, agency, empowerment, and violence. Further research is needed in LMICs for identifying and measuring the multiple domains of WHW.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".