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Record W2135143682

Setting priorities in global child health research investments: addressing values of stakeholders.

2007· article· en· W2135143682 on OpenAlexaff
Lydia Kapiriri, Mark Tomlinson, Mickey Chopra, Shams El Arifeen, Robert E. Black, Igor Rudan

Bibliographic record

VenuePubMed · 2007
Typearticle
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsUniversity of Toronto
FundersWorld Bank Group
KeywordsRelevance (law)Equity (law)StakeholderSustainabilityPublic relationsPsychologyPolitical science
DOInot available

Abstract

fetched live from OpenAlex

AIM: To identify main groups of stakeholders in the process of health research priority setting and propose strategies for addressing their systems of values. METHODS: In three separate exercises that took place between March and June 2006 we interviewed three different groups of stakeholders: 1) members of the global research priority setting network; 2) a diverse group of national-level stakeholders from South Africa; and 3) participants at the conference related to international child health held in Washington, DC, USA. Each of the groups was administered different version of the questionnaire in which they were asked to set weights to criteria (and also minimum required thresholds, where applicable) that were a priori defined as relevant to health research priority setting by the consultants of the Child Health and Nutrition Research initiative (CHNRI). RESULTS: At the global level, the wide and diverse group of respondents placed the greatest importance (weight) to the criterion of maximum potential for disease burden reduction, while the most stringent threshold was placed on the criterion of answerability in an ethical way. Among the stakeholders' representatives attending the international conference, the criterion of deliverability, answerability, and sustainability of health research results was proposed as the most important one. At the national level in South Africa, the greatest weight was placed on the criterion addressing the predicted impact on equity of the proposed health research. CONCLUSIONS: Involving a large group of stakeholders when setting priorities in health research investments is important because the criteria of relevance to scientists and technical experts, whose knowledge and technical expertise is usually central to the process, may not be appropriate to specific contexts and in accordance with the views and values of those who invest in health research, those who benefit from it, or wider society as a whole.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.271
metaresearch head score (Gemma)0.240
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.729
Threshold uncertainty score0.899

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2710.240
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0050.004
Science and technology studies0.0110.015
Scholarly communication0.0190.017
Open science0.0040.033
Research integrity0.0110.011
Insufficient payload (model declined to judge)0.0030.001

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 head0.821
GPT teacher head0.608
Teacher spread0.212 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designTheoretical or conceptual
DomainIncentives
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

Citations65
Published2007
Admission routes1
Has abstractyes

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