Socioeconomic differential in self-assessment of health and happiness in 5 African countries: Finding from World Value Survey
Bibliographic record
Abstract
OBJECTIVE: Factors that contribute to wealth related inequalities in self-rated health (SRH) and happiness remains unclear most especially in sub-Saharan countries (SSA). This study aims to explore and compare socioeconomic differentials in SRH and happiness in five SSA countries. METHODS: Using the 2010/2014 World Values Survey (WVS), we obtained a sample of 9,869 participants of age 16 and above from five SSA countries (Nigeria, Ghana, South Africa, Rwanda and Zimbabwe). Socioeconomic inequalities were quantified using the concentration index. The contribution of each predictor to concentration index's magnitude was obtained by means of regression based decomposition analysis. RESULTS: Poor SRH ranges from approximately 9% in Nigeria to 20% in Zimbabwe, whereas unhappiness was lower in Rwanda (9.5%) and higher in South Africa (23.3%). Concentration index was negative for both outcomes in all countries, which implies that poor SRH and unhappiness are excessively concentrated among the poorest socioeconomic strata. Although magnitudes differ across countries, however, the major contributor to wealth-related inequality in poor SRH is satisfaction with financial situation whereas for unhappiness the major contributors are level of income and satisfaction with financial situation. CONCLUSIONS: This study underscores an association between wealth related inequalities and poor SRH and unhappiness in the context of SSA. Improving equity in health, as suggested by the commission of social determinants of health may be useful in fighting against the unfair distribution of resources. Thus, knowledge about the self-rating of health and happiness can serve as proxy estimates for understanding the distribution of health care access and economic resources needed for well-being in resident countries.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".