The influence of corruption and governance in the delivery of frontline health care services in the public sector: a scoping review of current and future prospects in low and middle-income countries of south and south-east Asia
Bibliographic record
Abstract
Abstract Background The dynamic intersection of a pluralistic health system, large informal sector, and poor regulatory environment have provided conditions favourable for 'corruption' in the LMICs of south and south-east Asia region. 'Corruption' works to undermine the UHC goals of achieving equity, quality, and responsiveness including financial protection, especially while delivering frontline health care services. This scoping review examines current situation regarding health sector corruption at frontlines of service delivery in this region, related policy perspectives, and alternative strategies currently being tested to address this pervasive phenomenon. Methods A scoping review following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) was conducted, using three search engines i.e., PubMed, SCOPUS and Google Scholar . A total of 15 articles and documents on corruption and 18 on governance were selected for analysis. A PRISMA extension for Scoping Reviews (PRISMA-ScR) checklist was filled-in to complete this report. Data were extracted using a pre-designed template and analysed by 'mixed studies review' method. Results Common types of corruption like informal payments, bribery and absenteeism identified in the review have largely financial factors as the underlying cause. Poor salary and benefits, poor incentives and motivation, and poor governance have a damaging impact on health outcomes and the quality of health care services. These result in high out-of-pocket expenditure, erosion of trust in the system, and reduced service utilization. Implementing regulations remain constrained not only due to lack of institutional capacity but also political commitment. Lack of good governance encourage frontline health care providers to bend the rules of law and make centrally designed anti-corruption measures largely in-effective. Alternatively, a few bottom-up community-engaged interventions have been tested showing promising results. The challenge is to scale up the successful ones for measurable impact. Conclusions Corruption and lack of good governance in these countries undermine the delivery of quality essential health care services in an equitable manner, make it costly for the poor and disadvantaged, and results in poor health outcomes. Traditional measures to combat corruption have largely been ineffective, necessitating the need for innovative thinking if UHC is to be achieved by 2030.
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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".