Healthy public policy in poor countries: tackling macro-economic policies
Bibliographic record
Abstract
Large segments of the population in poor countries continue to suffer from a high level of unmet health needs, requiring macro-level, broad-based interventions. Healthy public policy, a key health promotion strategy, aims to put health on the agenda of policy makers across sectors and levels of government. Macro-economic policy in developing countries has thus far not adequately captured the attention of health promotion researchers. This paper argues that healthy public policy should not only be an objective in rich countries, but also in poor countries. This paper takes up this issue by reviewing the main macro-economic aid programs offered by international financial institutions as a response to economic crises and unmanageable debt burdens. Although health promotion researchers were largely absent during a key debate on structural adjustment programs and health during the 1980s and 1990s, the international macro-economic policy tool currently in play offers a new opportunity to participate in assessing these policies, ensuring new forms of macro-economic policy interventions do not simply reproduce patterns of (neoliberal) economics-dominated development policy.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".