Exploring the facilitators and barriers to achieving universal health coverage in Uganda: a qualitative study of the free healthcare policy
Bibliographic record
Abstract
BACKGROUND: Critical variations often occur between a state's initial public policy goals and its implementation outcomes. After two decades, the implementation of the free healthcare policy in Uganda has not achieved the desired outcomes, and there is a lack of comprehensive contextual analysis applying implementation science approaches in the identification of barriers and facilitators. This study explores barriers and facilitators to the implementation of the free healthcare policy, drawing on the retrospective experiences of policymakers, policy advocates, policy supporters or influencers, policy implementers, and policy beneficiaries. METHODS: We employed an exploratory qualitative study design and conducted 27 semi-structured interviews with key informants and 16 focus groups with users. Perspectives on implementation over time were collected by incorporating questions relating to the policy implementation journey from inception to 2023. The Consolidated Framework for Implementation Research guided data analysis to categorize and examine the barriers and facilitators to implementation. Two coders independently coded the data, which were thematically analysed with NVivo.14. RESULTS: A total of five main factors were identified, synthesized, and categorized as barriers and facilitators with overlaps, namely: (i) financial resources, (ii) medicines and supply system, (iii) health human resources, (iv) infrastructure and functionality, and (v) equity and the FHP Implementation. CONCLUSIONS: Findings illustrate that policy implementation gaps are due to limited resources, political will that does not translate into sufficient allocation of funds, and incremental policy shifts that are not driving meaningful improvement in the health system. The findings explain why the free healthcare policy implementation has been unsuccessful and highlight the importance of investing in resources to support meaningful progress towards universal health coverage.
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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 | 0.021 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.014 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".