The West African experience in establishing steering committees for better collaboration between researchers and decision-makers to increase the use of health research findings
Bibliographic record
Abstract
BACKGROUND: Aware of the advantages of a project steering committee (SC) in terms of influencing the development of evidence-based health policies, the West African Health Organisation (WAHO) encouraged and supported the creation of such SCs around four research projects in four countries (Burkina Faso, Nigeria, Senegal and Sierra Leone). This study was conducted to describe the process that was used to establish these committees and its findings aim to assist other stakeholders in initiating this type of process. METHODS: This is a cross-sectional, qualitative study of the initiative's four projects. In addition to a literature review and a review of the project documents, an interview guide was used to collect data from 14 members of the SCs, research teams, WAHO and the International Development Research Center. The respondents were selected with a view to reaching data saturation. The technique of thematic analysis by simple categorisation was used. RESULTS: To set up the SCs, a research team in each country worked with health authorities to identify potential members, organise meetings with these members and sought the authorities' approval to formalise the SCs. The SCs' mission was to provide technical assistance to the researchers during the implementation phase and to facilitate the transfer and use of the findings. The 'doing by learning' approach used by each research team, combined with WAHO's catalytic role with each country's Ministry of Health, helped each SC manage its contextual difficulties and function effectively. CONCLUSION: The involvement of technical and financial partners motivated the researchers and ministries of health, who, in turn, motivated other actors to volunteer on the SCs. The 'doing by learning' approach made it possible to develop strategies adapted to each context to create, facilitate and operate each SC and manage its difficulties. To reproduce such an experience, a strong understanding of the local context and the involvement of strong partners are required.
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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 | Scholarly communication Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.105 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".