Affording what's free and paying for choice: comparing the cost of public and private hospitalizations in urban Kerala
Bibliographic record
Abstract
OBJECTIVE: To assess the cost of public and private hospitalizations in urban Kerala and discuss policy implications of social disparities in the economic burden of hospital care. METHODS: The NSSO survey on health care (1995-1996) for urban Kerala was analysed with regards to expenditure incurred by hospital episodes. Multilevel linear models were built to assess factors associated with levels of health expenditure. FINDINGS: Hospital care involves paying admission fees in 68% of cases of hospitalizations (98% in private and 20% in public sector) in urban Kerala. Poor households and those headed by casual workers show significantly lower levels of health expenditure and a higher proportion of health-related loss of income than other social groups. Although there is significant expenditure in both sectors for these groups, hospitalization on free public wards is associated with lower expenditure than other options. Factors linked with higher expenditure are: duration of stay; hospitalizations on paying public wards and in the private sector; hospitalizations for above poverty line households and hospitalizations for chronic illnesses. Expenditure for services bought from outside the hospital is important in the public sector. CONCLUSION: Hospitalization incurs significant expenditure in urban Kerala. Greater availability of free medical services in the public sector and financial protection against the cost of hospitalization are warranted.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".