Ethical health leadership: Lessons from low- and middle-income countries during COVID-19
Bibliographic record
Abstract
We adopt a holistic-micro, meso, macro-approach to health leadership ethics to examine how low- and middle-income countries have responded to the COVID-19 pandemic. Healthcare delivery happens within complex settings in low- and middle-income countries and high-income countries. These settings are riddled with systemic political and economic challenges which, in some instances, make it difficult for health leaders to be ethical. These challenges, however, are not unique to low- and middle-income countries. Globally, countries can learn from ethical health leadership missteps that occurred during low- and middle-income countries' responses to COVID-19. We discuss the implications of problematic ethics in health leadership on managing pandemics in low- and middle-income countries, using Zimbabwe as an example. We offer suggestions on what can be done to improve ethical health leadership in response to future health crises in both high-income and low- and middle-income nations.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".