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Record W2138760408 · doi:10.1093/heapro/dam008

Healthy public policy in poor countries: tackling macro-economic policies

2007· article· en· W2138760408 on OpenAlexaff
K. S. Mohindra

Bibliographic record

VenueHealth Promotion International · 2007
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Public Health Policies and Epidemiology
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsHealth policyHealth promotionMacroPublic policyPublic healthGovernment (linguistics)Economic growthPsychological interventionPromotion (chess)Developing countryPublic economicsEconomicsBusinessPolitical scienceDevelopment economicsHealth careMedicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.810
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.050
GPT teacher head0.378
Teacher spread0.328 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations21
Published2007
Admission routes1
Has abstractyes

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