The Health Sector in Jordan: Effectiveness and Efficiency
Bibliographic record
Abstract
This paper compares a homogeneous group of countries in terms of capacity and technology, where we picked income as indicator for capacity and technology. We study the case of the Hashemite Kingdom of Jordan. We apply radical data envelopment analysis to 36 middle income countries where we calculate constant returns to scale technical efficiency and variable returns to scale technical efficiency to show the health care sector efficiency in Jordan. Using different factors for input first we studied healthcare expenditure per capita then as percent of GDP and public expenditure as percent of GDP and private as percent of GDP, and last was the number of beds per 1000 population and physicians per 1000 population all to the same output life expectancy. The results show that there is inefficiency in health care expenditure. The inefficiency mainly is shown by two major findings, first the lack of utilization of resources. Secondly, the public-sector inefficiency. The output is justifiable for many challenges faced the health sector in the year of the study one of which is the Syrian refuges crisis. We shed light on factors causing the inefficiency where modifications could yield substantial efficiency gains. As for the mix between public and private sectors and the quality and utilization and distribution of the real resources, nevertheless adding health economists to the management staff for there is a managerial inefficiency.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".