Implementation of a health policy advisory committee as a knowledge translation platform: the Nigeria experience
Bibliographic record
Abstract
BACKGROUND: In recent times, there has been a growing demand internationally for health policies to be based on reliable research evidence. Consequently, there is a need to strengthen institutions and mechanisms that can promote interactions among researchers, policy-makers and other stakeholders who can influence the uptake of research findings. The Health Policy Advisory Committee (HPAC) is one of such mechanisms that can serve as an excellent forum for the interaction of policy-makers and researchers. Therefore, the need to have a long term mechanism that allows for periodic interactions between researchers and policy-makers within the existing government system necessitated our implementation of a newly established HPAC in Ebonyi State Nigeria, as a Knowledge Translation (KT) platform. The key study objective was to enhance the capacity of the HPAC and equip its members with the skills/competence required for the committee to effectively promote evidence informed policy-making and function as a KT platform. METHODS: A series of capacity building programmes and KT activities were undertaken including: i) Capacity building of the HPAC using Evidence-to-Policy Network (EVIPNet) SUPPORT tools; ii) Capacity enhancement mentorship programme of the HPAC through a three-month executive training programme on health policy/health systems and KT in Ebonyi State University Abakaliki; iii) Production of a policy brief on strategies to improve the performance of the Government's Free Maternal and Child Health Care Programme in Ebonyi State Nigeria; and iv) Hosting of a multi-stakeholders policy dialogue based on the produced policy brief on the Government's Free Maternal and Child Health Care Programme. RESULTS: The study findings indicated a noteworthy improvement in knowledge of evidence-to-policy link among the HPAC members; the elimination of mutual mistrust between policy-makers and researchers; and an increase in the awareness of importance of HPAC in the Ministry of Health (MoH). CONCLUSION: Findings from this study suggest that a HPAC can function as a KT platform and can introduce a new dimension towards facilitating evidence-to-policy link into the operation of the MoH, and can serve as an excellent platform to bridge the gap between research and policy.
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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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".