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Seven Challenges in International Development Assistance for Health and Ways Forward

2010· article· en· W2162857663 on OpenAlexaff
Devi Sridhar

Bibliographic record

VenueThe Journal of Law Medicine & Ethics · 2010
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Public Health Policies and Epidemiology
Canadian institutionsInstitute on Governance
Fundersnot available
KeywordsAccountabilityContext (archaeology)Developing countryUnintended consequencesBusinessEconomic growthQuality (philosophy)Aid effectivenessHealth policyDevelopment aidPolitical sciencePublic relationsHealth careFinanceEconomics

Abstract

fetched live from OpenAlex

This paper outlines seven challenges in development assistance for health, which in the current financial context, have become even more important to address. These include the following: (1) the proliferation of initiatives, focusing on specific diseases or issues, as well as (2) the lack of attention given to reforming the existing focal health institutions, the WHO and World Bank. (3) The lack of accountability of donors and their influence on priority-setting are part of the reason that there is "initiavitis," and resistance to creating a strong UN system. (4) Other than absolute quantity of aid, three other challenges linked to donors relate to the quality of aid financing particularly the pragmatic difficulties of financing horizontal interventions, (5) the marginal involvement of developing country governments as aid recipients, and (6) the heavy reliance on Northern-based organizations as managers of funds. (7) The final challenge discussed focuses on two unintended consequences of the recent linking of health and foreign policy for international development assistance. The paper then provides three suggestions for ways forward: creating new mechanisms to hold donors to account, developing national plans and strengthening national leadership in health, and South-South collaboration.

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)0.011
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.914
Threshold uncertainty score0.392

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.211
GPT teacher head0.414
Teacher spread0.203 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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

Citations55
Published2010
Admission routes1
Has abstractyes

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