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Record W4410498658 · doi:10.1186/s12961-025-01334-8

Exploring the facilitators and barriers to achieving universal health coverage in Uganda: a qualitative study of the free healthcare policy

2025· article· en· W4410498658 on OpenAlexafffund
Prossy Kiddu Namyalo, Cyndirela Chadambuka, Lisa Forman, Beverley M. Essue, Freddie Ssengooba

Bibliographic record

VenueHealth Research Policy and Systems · 2025
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsPublic Health OntarioUniversity of Toronto
FundersInternational Development Research Centre
KeywordsHealth policyHealth services researchEquity (law)Health careFocus groupQualitative researchInfluencer marketingHealth administrationImplementation researchPublic relationsSocial policyPublic policyPolicy analysisMedicinePublic healthPublic administrationPolitical scienceNursingBusinessSociologyEconomic growthMarketingEconomicsPsychological intervention

Abstract

fetched live from OpenAlex

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.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.021
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.111

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.026
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0150.014
Scholarly communication0.0050.006
Open science0.0020.008
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.288
GPT teacher head0.517
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2025
Admission routes2
Has abstractyes

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