Insular pathways to health care in the city: a multilevel analysis of access to hospital care in urban Kerala, India
Bibliographic record
Abstract
OBJECTIVES: To identify individual and urban unit characteristics associated with access to inpatient care in public and private sectors in urban Kerala, and to discuss policy implications of inequalities in access. METHODS: We analysed the NSSO survey (1995-1996) for urban Kerala with regard to source and trajectories of hospitalization. Multinomial multilevel regression models were built for 695 cases nested in 24 urban units. RESULTS: Private sector accounts for 62% of hospitalizations. Only 31% of hospitalizations are in free wards and 20% of public hospitalizations involve payment. Hospitalization pathways suggest a segmentation of public and private health markets. Members of poor and casual worker households have lower propensity of hospitalization in paying public wards or private hospitals. There were important variations between cities, with higher odds of private hospitalization in towns with fewer hospital beds overall and in districts with high private-public bed ratios. Cities from districts with better economic indicators and dominance of private services have higher proportion of private hospitalizations. CONCLUSIONS: The private sector is the predominant source of inpatient care in urban Kerala. The public sector has an important role in providing access to care for the poor. Investing in the quality of public services is essential to ensure equity in access.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".