Schools of public health in low and middle-income countries: an imperative investment for improving the health of populations?
Bibliographic record
Abstract
BACKGROUND: Public health has multicultural origins. By the close of the nineteenth century, Schools of Public Health (SPHs) began to emerge in western countries in response to major contemporary public health challenges. The Flexner Report (1910) emphasized the centrality of preventive medicine, sanitation, and public health measures in health professional education. The Alma Ata Declaration on Primary Health Care (PHC) in 1978 was a critical milestone, especially for low and middle-income countries (LMICs), conceptualizing a close working relationship between PHC and public health measures. The Commission on Social Determinants of Health (2005-2008) strengthened the case for SPHs in LMICs as key stakeholders in efforts to reduce global health inequities. This scoping review groups text into public health challenges faced by LMICs and the role of SPHs in addressing these challenges. MAIN TEXT: The challenges faced by LMICs include rapid urbanization, environmental degradation, unfair terms of global trade, limited capacity for equitable growth, mass displacements associated with conflicts and natural disasters, and universal health coverage. Poor governance and externally imposed donor policies and agendas, further strain the fragile health systems of LMICs faced with epidemiological transition. Moreover barriers to education and research imposed by limited resources, political and economic instability, and unbalanced partnerships additionally aggravate the crisis. To address these contextual challenges effectively, SPHs are offering broad based health professional education, conducting multidisciplinary population based research and fostering collaborative partnerships. SPHs are also looked upon as the key drivers to achieve sustainable development goals (SDGs). CONCLUSION: SPHs in LMICs can contribute to overcoming several public health challenges being faced by LMICs, including achieving SDGs. Most importantly they can develop cadres of competent and well-motivated public health professionals: educators, practitioners and researchers who ask questions that address fundamental health determinants, seek solutions as agents of change within their mandates, provide specific services and serve as advocates for multilevel partnerships. Funding support, human resources, and agency are unfortunately often limited or curtailed in LMICs, and this requires constructive collaboration between LMICs and counterpart institutions from high income countries.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".