Determinants of out‐of‐pocket expenditures on prescribed medications in Tajikistan
Bibliographic record
Abstract
PURPOSE: The purpose of this paper is to quantify the impact of socio-economic characteristics on out-of pocket expenditures for prescribed medications in Tajikistan and provide recommendations for healthcare sector reform. The research question in this paper is: what household, personal, economic, and health factors help explain expenditures on medications? From a theoretical perspective, this paper contributes to the on-going discussion of out-of-pocket expenditures in Tajikistan. From a practical perspective, in line with this recent development in the Tajikistan healthcare sector, it helps to develop evidence-based decision-making by answering practical questions: what factors affect pattern of out-of-pocket expenditures for prescribed medication? Which groups of the population should be granted a discount or fee-waiver when buying them? DESIGN/METHODOLOGY/APPROACH: Based on micro-file data from the most recent cross-sectional nationally-representative survey of Tajik households, this paper develops and tests a multivariate model of identifying determinants of out-of-pocket expenditures on prescribed medications in Tajikistan. FINDINGS: The paper finds that economic status, chronic illness, disability, number of small children, short supply of necessary drugs, and cardiac and acute illnesses are the strongest determinants of spending for prescribed medications in the country. ORIGINALITY/VALUE: This paper demonstrates that to ensure accessibility to and affordability of prescribed medications, discounts or fee-waivers should be granted to specific categories of households, those in poverty, with chronically ill members and with small children. These discounts or fee-waivers should cover prescribed medications for children, long-standing illness as well as for cardiac and acute infectious diseases. Administrative and economic measures should be taken to reduce the extra costs incurred due to the shortage of prescribed medications. Hence, these findings can be used in developing and designing reforms in the Tajikistan healthcare sector.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".