Women’s health in a rural community in Kerala, India: do caste and socioeconomic position matter?
Bibliographic record
Abstract
OBJECTIVES: To examine the social patterning of women's self-reported health status in India and the validity of the two hypotheses: (1) low caste and lower socioeconomic position is associated with worse reported health status, and (2) associations between socioeconomic position and reported health status vary across castes. DESIGN: Cross-sectional household survey, age-adjusted percentages and odds ratios, and multilevel multinomial logistic regression models were used for analysis. SETTING: A panchayat (territorial decentralised unit) in Kerala, India, in 2003. PARTICIPANTS: 4196 non-elderly women. OUTCOME MEASURES: Self-perceived health status and reported limitations in activities in daily living. RESULTS: Women from lower castes (scheduled castes/scheduled tribes (SC/ST) and other backward castes (OBC) reported a higher prevalence of poor health than women from forward castes. Socioeconomic inequalities were observed in health regardless of the indicators, education, women's employment status or household landholdings. The multilevel multinomial models indicate that the associations between socioeconomic indicators and health vary across caste. Among SC/ST and OBC women, the influence of socioeconomic variables led to a "magnifying" effect, whereas among forward caste women, a "buffering" effect was found. Among lower caste women, the associations between socioeconomic factors and self-assessed health are graded; the associations are strongest when comparing the lowest and highest ratings of health. CONCLUSIONS: Even in a relatively egalitarian state in India, there are caste and socioeconomic inequalities in women's health. Implementing interventions that concomitantly deal with caste and socioeconomic disparities will likely produce more equitable results than targeting either type of inequality in isolation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".