Who is in and who is out? A qualitative analysis of stakeholder participation in priority setting for health in three districts in Uganda
Bibliographic record
Abstract
Stakeholder participation is relevant in strengthening priority setting processes for health worldwide, since it allows for inclusion of alternative perspectives and values that can enhance the fairness, legitimacy and acceptability of decisions. Low-income countries operating within decentralized systems recognize the role played by sub-national administrative levels (such as districts) in healthcare priority setting. In Uganda, decentralization is a vehicle for facilitating stakeholder participation. Our objective was to examine district-level decision-makers' perspectives on the participation of different stakeholders, including challenges related to their participation. We further sought to understand the leverages that allow these stakeholders to influence priority setting processes. We used an interpretive description methodology involving qualitative interviews. A total of 27 district-level decision-makers from three districts in Uganda were interviewed. Respondents identified the following stakeholder groups: politicians, technical experts, donors, non-governmental organizations (NGO)/civil society organizations (CSO), cultural and traditional leaders, and the public. Politicians, technical experts and donors are the principal contributors to district-level priority setting and the public is largely excluded. The main leverages for politicians were control over the district budget and support of their electorate. Expertise was a cross-cutting leverage for technical experts, donors and NGO/CSOs, while financial and technical resources were leverages for donors and NGO/CSOs. Cultural and traditional leaders' leverages were cultural knowledge and influence over their followers. The public's leverage was indirect and exerted through electoral power. Respondents made no mention of participation for vulnerable groups. The public, particularly vulnerable groups, are left out of the priority setting process for health at the district. Conflicting priorities, interests and values are the main challenges facing stakeholders engaged in district-level priority setting. Our findings have important implications for understanding how different stakeholder groups shape the prioritization process and whether representation can be an effective mechanism for participation in health-system priority setting.
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 | 0.019 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".