Corruption in Health Systems: The Conversation Has Started, Now Time to Continue it Comment on "We Need to Talk About Corruption in Health Systems"
Bibliographic record
Abstract
Holistic and multi-disciplinary responses should be prioritized given the depth and breadth through which corruption in the healthcare sector can cover. Here, taking the Peruvian context as an example, we will reflect on the issue of corruption in health systems, including corruption with roots within and outside the health sector, and ongoing efforts to combat it. Our reflection of why corruption in health systems in settings with individual and systemic corruption should be an issue that is taken more seriously in Peru and beyond aligns with broader global health goals of improving health worldwide. Addressing corruption also serves as a pragmatic approach to health system strengthening and weakens a barrier to achieving universal health coverage and Sustainable Development Goals related to health and justice. Moreover, we will argue that by pushing towards a practice of normalizing the conversation about corruption in health has additional benefits, including expanding the problematization to a wider audience and therefore engaging with communities. For young researchers and global health professionals with interests in improving health systems in the early career stages, corruption in health systems is an issue that could move to the forefront of the list of global health challenges. This is a challenge that is uniquely multi-disciplinary, spanning the health, economy, and legal sectors, with wider societal implications.
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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 | 0.009 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.046 | 0.051 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".