Assessing the influence of knowledge translation platforms on health system policy processes to achieve the health millennium development goals in Cameroon and Uganda: a comparative case study
Bibliographic record
Abstract
There is a scarcity of empirical data on the influence of initiatives supporting evidence-informed health system policy-making (EIHSP), such as the knowledge translation platforms (KTPs) operating in Africa. To assess whether and how two KTPs housed in government-affiliated institutions in Cameroon and Uganda have influenced: (1) health system policy-making processes and decisions aiming at supporting achievement of the health millennium development goals (MDGs); and (2) the general climate for EIHSP. We conducted an embedded comparative case study of four policy processes in which Evidence Informed Policy Network (EVIPNet) Cameroon and Regional East African Community Health Policy Initiative (REACH-PI) Uganda were involved between 2009 and 2011. We combined a documentary review and semi structured interviews of 54 stakeholders. A framework-guided thematic analysis, inspired by scholarship in health policy analysis and knowledge utilization was used. EVIPNet Cameroon and REACH-PI Uganda have had direct influence on health system policy decisions. The coproduction of evidence briefs combined with tacit knowledge gathered during inclusive evidence-informed stakeholder dialogues helped to reframe health system problems, unveil sources of conflicts, open grounds for consensus and align viable and affordable options for achieving the health MDGs thus leading to decisions. New policy issue networks have emerged. The KTPs indirectly influenced health policy processes by changing how interests interact with one another and by introducing safe-harbour deliberations and intersected with contextual ideational factors by improving access to policy-relevant evidence. KTPs were perceived as change agents with positive impact on the understanding, acceptance and adoption of EIHSP because of their complementary work in relation to capacity building, rapid evidence syntheses and clearinghouse of policy-relevant evidence. This embedded case study illustrates how two KTPs influenced policy decisions through pathways involving policy issue networks, interest groups interaction and evidence-supported ideas and how they influenced the general climate for EIHSP.
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How this classification was reachedexpand
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 | 0.058 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 source (direct Gemma or distilled Codex), 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".