Women’s environmental quality of life is key to their overall quality of life and health: Global evidence from the WHOQOL-100
Bibliographic record
Abstract
Gender inequalities in health-related quality of life (QoL) are generally few and small, even in large surveys. Many generic measures limit assessment to QoL overall and its physical and psychological dimensions, while overlooking internationally important environmental, social, and spiritual QoL domains. Unique cross-cultural legacy data was collected using four WHOQOL-100 surveys of adults living in 43 cultures world-wide (17,608 adults; ages 15-101). It was first used to examined gender profiles of its five QoL international domains, and their component facets. Few significant gender differences (p < .001) were found. Women reported higher spiritual QoL than men on faith, and spiritual connection facets specifically. Men reported higher physical and psychological QoL domains than women. We aimed to identify those QoL dimensions that contribute to women's overall QoL in health, as this information could inform gender inequalities interventions in health. Environmental QoL explained a substantial 46% of women's overall QoL and health (n = 5,017; 17 cultures) (stepwise multiple regression adjusted for age, education, and marital status covariates). Five environmental QoL facets contributed significantly to this result; home environment offered most explanation. Age band analysis was conducted to understand when interventions might be best timed in the lifespan to improve women's QoL. Younger women (< 45 years) reported the poorest QoL across the lifetime, and on every domain. After 45, all domains except physical QoL increased to very good at about 60, and high levels were sustained beyond 75, especially environmental QoL. Global findings show that assessing environmental, social, and spiritual QoL domains are key to fully understanding women's QoL and health. These assessments should be prioritized in surveys that aim to improve international conservation, and public health policies.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".