Postavljanje prioriteta pri globalnom ulaganju u istraživanja o dječjem zdravlju: određivanje sustava vrijednosti zainteresiranih strana
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | medium |
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".