Analyzing the influence of institutions on health policy development in Uganda: a case study of the decision to abolish user fees.
Bibliographic record
Abstract
BACKGROUND: During the 2001 election campaign, President Yoweri Museveni announced he was abolishing user fees for health services in Uganda. No analysis has been carried out to explain how he was able to initiate such an important policy decision without encountering any immediate barriers. OBJECTIVE: To explain this outcome through in-depth policy analysis driven by the application of key analytical frameworks. METHODS: An explanatory case study informed by analytical frameworks from the institutionalism literature was undertaken. Multiple data sources were used including: academic literature, key government documents, grey literature, and a variety of print media. RESULTS: According to the analytical frameworks employed, several formal institutional constraints existed that would have reduced the prospects for the abolition of user fees. However, prevalent informal institutions such as "Big Man" presidentialism and clientelism that were both 'competing' and 'complementary' can be used to explain the policy outcome. The analysis suggests that these factors trumped the impact of more formal institutional structures in the Ugandan context. CONCLUSION: Consideration should be given to the interactions between formal and informal institutions in the analysis of health policy processes in Uganda, as they provide a more nuanced understanding of how each set of factors influence policy outcomes.
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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 | 0.016 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".