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Record W2735696000 · doi:10.1186/s12961-017-0214-8

Evaluation of regional project to strengthen national health research systems in four countries in West Africa: lessons learned

2017· article· en· W2735696000 on OpenAlexfundno aff
Issiaka Sombié, Jude Aidam, Gabriela Montorzi

Bibliographic record

VenueHealth Research Policy and Systems · 2017
Typearticle
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsnot available
FundersInternational Development Research Centre
KeywordsContext (archaeology)Health services researchCorporate governanceCitizen journalismSierra leonePolitical sciencePublic administrationEconomic growthEnvironmental resource managementEnvironmental planningBusinessPublic healthMedicineSociologyGeographySocioeconomicsNursingFinance

Abstract

fetched live from OpenAlex

BACKGROUND: Since the Commission on Health Research for Development (COHRED) published its flagship report, more attention has been focused on strengthening national health research systems (NHRS). This paper evaluates the contribution of a regional project that used a participatory approach to strengthen NHRS in four post-conflict West African countries - Guinea-Bissau, Liberia, Sierra Leone and Mali. METHODS: The data from the situation analysis conducted at the start of the project was compared to data from the project's final evaluation, using a hybrid conceptual framework built around four key areas identified through the analysis of existing frameworks. The four areas are governance and management, capacities, funding, and dissemination/use of research findings. RESULTS: The project helped improve the countries' governance and management mechanisms without strengthening the entire NHRS. In the four countries, at least one policy, plan or research agenda was developed. One country put in place a national health research ethics committee, while all four countries could adopt a research information management system. The participatory approach and support from the West African Health Organisation and COHRED were all determining factors. CONCLUSION: The lessons learned from this project show that the fragile context of these countries requires long-term engagement and that support from a regional institution is needed to address existing challenges and successfully strengthen the entire NHRS.

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)0.131
metaresearch head score (Gemma)0.021
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.529
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1310.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.001
Science and technology studies0.0010.000
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 head0.847
GPT teacher head0.645
Teacher spread0.201 · 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; both teacher heads agree on what is shown here.

Study designObservational
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

Citations23
Published2017
Admission routes1
Has abstractyes

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