Evaluating the Process and Extent of Institutionalization: A Case Study of a Rapid Response Unit for Health Policy in Burkina Faso
Bibliographic record
Abstract
BACKGROUND: Good decision-making requires gathering and using sufficient information. Several knowledge translation platforms have been introduced in Burkina Faso to support evidence-informed decision-making. One of these is the rapid response service for health. This platform aims to provide quick access for policy-makers in Burkina Faso to highquality research evidence about health systems. The purpose of this study is to describe the process and extent of the institutionalization of the rapid response service. METHODS: A qualitative case study design was used, drawing on interviews with policy-makers, together with documentary analysis. Previously used institutionalization frameworks were combined to guide the analysis. RESULTS: Burkina Faso's rapid response service has largely reached the consolidation phase of the institutionalization process but not yet the final phase of maturity. The impetus for the project came from designated project leaders, who convinced policy-makers of the importance of the rapid response service, and obtained resources to run a pilot. During the expansion stage, additional policy-makers at national and sub-national levels began to use the service. Unit staff also tried to improve the way it was delivered, based on lessons learned during the pilot stage. The service has, however, stagnated at the consolidation stage, and not moved into the final phase of maturity. CONCLUSION: The institutionalization process for the rapid response service in Burkina Faso has been fluid rather than linear, with some areas developing faster than others. The service has reached the consolidation stage, but now requires additional efforts to reach maturity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".