Understanding the Context of Global Health Policies
Bibliographic record
Abstract
The systemic inadequacies of models of health systems propagated by the advocates of global health policies (GHPs) have fragmented health service systems, particularly in middle- and lower-income countries. GHPs are underpinned by economic interests and the need for control by the global elite, irrespective of people’s health needs. The COVID-19 pandemic challenged the advocates of GHPs, leading to calls for a movement for “decolonisation” of global health. Much of this narrative on the “decolonisation” of GHPs critiques its northern knowledge base, and the power derived from it at individual, institutional and national levels. This, it argues, has led to an unequal exchange of knowledge, making it impossible to end decades of oppressive hegemony and to prevent inappropriate decision-making on GHPs. Despite these legitimate concerns, little in the literature on the decolonisation of GHPs extends beyond epistemological critiques. This article offers a radically different perspective. It is based on an understanding of the role of transnational capital in extracting wealth from the economies of low- and middle-income countries resulting in influencing and shaping public health policy and practice, including interactions between the environment and health. It mobilises historical evidence of distorted priorities underpinning GHPs and the damaging consequences for health services throughout the world.
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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 | 0.017 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.006 | 0.049 |
| Scholarly communication | 0.022 | 0.018 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".