Policy entrepreneurs and structural influence in integrated community case management policymaking in Burkina Faso
Bibliographic record
Abstract
Policy entrepreneurs are individuals who attempt to influence the policy process and its outcomes through their opportunistic or incremental actions. Their success in the policy-making process has been associated with the convergence of four factors: behavioural traits; institutional factors; network position and political capital. Policy entrepreneurs have received little study in low- and middle-income country policy research despite observations of individualized decision-making, informal institutions and the unequal distribution and exercise of power in policymaking. This article aims to identify whether policy entrepreneurs were present in the policy process around integrated community case management (iCCM) in Burkina Faso, whether they were successful in achieving policy change, and whether success or failure can be explained using existing policy entrepreneur frameworks from high-income polities. This mixed methods policy study collected data from in-depth qualitative interviews and social network surveys of actors involved in iCCM policymaking [known locally as C-integrated management of childhood illness (IMCI)]; data were analysed based on the framework categories. Interview data pointed to one key individual who played a significant role in the inclusion of pneumonia treatment into the country's iCCM policy, an issue that had been a point of contention between government policy elites and development partners. Social network data confirmed that this actor was strategically located in the policy network to be able to reach the most other actors and to be able to control the flow of information. Although some development partner actors were as strategically located, none had the same level of authority or trust as was imbued by being a member of the government civil service. The entrepreneur's mid-level rank in the health ministry may have encouraged him/her to invest political capital and take risks that would not have been feasible or attractive to a more senior actor. This study highlights the convergence of factors needed to be an entrepreneur, as well as the role of development partner actors in creating a facilitating environment.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".