Integrity and Corruption in the Health Sector in Jordan: The Perceptions of Leaders of Non-government Health Organizations (NGHOs)
Bibliographic record
Abstract
Corruption is a complex social and economic phenomenon which does not only threaten equity, but also health outcomes. This study aims at identifying corruption practices in the health sector in Jordan, factors that promote these practices and policy directions to control them as perceived by leaders of non–government health care organizations (NGHOs). The study adopted both qualitative and quantitative approaches. 24 NGHOs leaders participated in a one -day workshop and were divided into three sub-groups to address areas of corruption in the health sector in Jordan based on a conceptual model which addresses corruption according to the main actors, namely: regulators, providers, payers, patients and suppliers. The findings of the three sub-groups were put together by the researcher and were sent to the participants by email for validation and ranking.The results showed that organizers’ corruption was mainly manifested in favoritism, seeking personal interest, failure to base decisions on evidence and accepting bribes from suppliers. Corruption among providers was perceived mainly in nepotism and favoritism among doctors, especially in malpractice cases, evasion of taxes and fees and overcharging patients. Corruption caused by suppliers was manifested in tax evasion, bribing and fraud. Corruption caused by patients was perceived in trying to get free care by under reporting their income, deceiving insurers to obtain benefits and stealing and vandalism. Corruption related to health insurers was manifested in tax evasion, incapacitating patients and delaying approvals of claims and unjustified deductions on patients’ bills. Causes of corruption and interventions to improve integrity in the health sector were also addressed by the participants.
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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 | 0.004 | 0.001 |
| 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".