Hospitalization in Tajikistan: determinants of admission, length of stay, and out‐of‐pocket expenditures. Results of a national survey
Bibliographic record
Abstract
OBJECTIVE: To assess factors explaining hospitalization in Tajikistan and discuss policy implications for reforms in hospital care. METHODS: This study involves a secondary analysis of micro-data collected from a nationally-representative household survey conducted in Tajikistan in 2003. Three empirical models are employed: binomial logit regression for the admission to the hospital; zero truncated negative binomial (ZTNB) regression for the length of hospital stay; and ordinary least square (OLS) for the amount of out-of-pocket expenditures for hospitalization. FINDINGS: Variation in hospital admission is due to the differences in ability to pay, long-standing illness, gender, age, and educational level. Factors explaining out-of-pocket expenditures include ability to pay, having long-standing illness, and having surgery and receiving intensive care. The most important out-of-pocket expenditures are payments for pharmaceuticals and supplies. Finally, long hospital stay that is the result of outdated treatment protocols, rigid financial and management system, lack of funding, and weakness of primary and long-tem care. As a result, long time in inpatient care is mostly used ineffectively. CONCLUSION: Strategies to address the existing deficiencies include voluntary community-based health insurance for rural areas, targeted subsidized care for the neediest, improvements in procurement of pharmaceuticals and supplies, and rationalization of hospital primary and long-term care.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".