Wealth Inequality as a Predictor of Subjective Health, Happiness and Life Satisfaction among Nepalese Women
Bibliographic record
Abstract
Socioeconomic status has shown to be associated with subjective health, well-being, satisfaction with overall life and estimation of happiness. The body of research concerning the question of whether higher economic status leads to better health and well-being are mostly from developed countries. The present study was therefore conducted among women in Nepal with an aim to investigate whether household wealth status is associated with satisfaction about (1) self-reported health, (2) happiness, and (3) life overall. Methods: Subjects were 5226 Nepalese women aged between 15 and 24 years. Cross-sectional data were extracted from round 5 of the Nepal Multiple Indicator Cluster Survey (NMICS), conducted in 2014, and analyzed using chi-square tests of association, bivariate and multivariable regression methods. Results: Wealth status was significantly associated with satisfaction about health, estimation of happiness and satisfaction. Compared with women in the poorest households, the odds of positive estimation about overall happiness were respectively 30% higher for poorer (p < 0.0001; 95% CI = 1.653–3.190), 80% higher for middle (p = 0.001; 95% CI = 1.294–2.522), 64% higher for richer (p = 0.006; 95% CI = 1.155–2.326), and 40% higher for richest households. The odds of reporting satisfaction about life were respectively 97% higher for poorer (p < 0.0001; 95% CI = 1.680–2.317), 41% higher for middle (p < 0.0001; 95% CI = 1.165–1.715), 62% higher for richer (p < 0.0001; 95% CI = 1.313–2.003), and 31% higher for richest households (p = 0.043; 95% CI = 1.008–1.700). Conclusion: Our results conclude that women in households with lower wealth status report poorer subjective health, quality of life and happiness. However, the findings need to be interpreted in light of the existing sociocultural conditions mediating the role of household wealth status on women’s lives.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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 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".