Setting weights for fifteen CHNRI criteria at the global and regional level using public stakeholders: an Amazon Mechanical Turk study
Bibliographic record
Abstract
INTRODUCTION: Stakeholder involvement has been described as an indispensable part of health research priority setting. Yet, more than 75% of the exercises using the Child Health and Nutrition Research Initiative (CHNRI) methodology have omitted the step involving stakeholders in priority setting. Those that have used stakeholders have rarely used the public, possibly due to the difficulty of assembling and/or accessing a public stakeholder group. In order to strengthen future exercises using the CHNRI methodology, we have used a public stakeholder group to weight 15 CHNRI criteria, and have explored regional differences or being a health stakeholder is influential, and whether the criteria are collapsible. METHODS: Using Amazon Mechanical Turk (AMT), an online crowdsourcing platform, we collected demographic information and conducted a Likert-scale format survey about the importance of the CHNRI criteria from 1051 stakeholders. The Kruskal-Wallis test, with Dunn's test for posthoc comparisons, was used to examine regional differences and Wilcoxon rank-sum test was used to analyse differences between stakeholders with health training/background and stakeholders without a health background and by region. A Factor Analysis (FA) was conducted on the criteria to identify the main domains connecting them. Criteria means were converted to weights. RESULTS: There were regional differences in thirteen of fifteen criteria according to the Kruskal-Wallis test and differences in responses from health stakeholders vs those who were not in eleven of fifteen criteria using the Wilcoxon rank-sum test. Three components were identified: improve and impact results; implementation and affordability; and, study design and dissemination. A formula is provided to convert means to weights for future studies. CONCLUSION: In future CHNRI studies, researchers will need to ensure adequate representation from stakeholders to undue bias of CHNRI results. These results should be used in combination with other stakeholder groups, including government, donors, policy makers, and bilateral agencies. Global and regional stakeholder groups scored CHNRI criteria differently; due to this, researchers should consider which group to use in their CHNRI exercises.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".