Barriers to Equitable Public Participation in Health-System Priority Setting Within the Context of Decentralization: The Case of Vulnerable Women in a Ugandan District
Bibliographic record
Abstract
BACKGROUND: Decentralization of healthcare decision-making in Uganda led to the promotion of public participation. To facilitate this, participatory structures have been developed at sub-national levels. However, the degree to which the participation structures have contributed to improving the participation of vulnerable populations, specifically vulnerable women, remains unclear. We aim to understand whether and how vulnerable women participate in health-system priority setting; identify any barriers to vulnerable women's participation; and to establish how the barriers to vulnerable women's participation can be addressed. METHODS: We used a qualitative description study design involving interviews with district decision-makers (n=12), sub-county leaders (n=10), and vulnerable women (n=35) living in Tororo District, Uganda. Data was collected between May and June 2017. The analysis was conducting using an editing analysis style. RESULTS: The vulnerable women expressed interest in participating in priority setting, believing they would make valuable contributions. However, both decision-makers and vulnerable women reported that vulnerable women did not consistently participate in decision-making, despite participatory structures that were instituted through decentralization. There are financial (transportation and lack of incentives), biomedical (illness/disability and menstruation), knowledge-based (lack of knowledge and/or information about participation), motivational (perceived disinterest, lack of feedback, and competing needs), socio-cultural (lack of decision-making power), and structural (hunger and poverty) barriers which hamper vulnerable women's participation. CONCLUSION: The identified barriers hinder vulnerable women's participation in health-system priority setting. Some of the barriers could be addressed through the existing decentralization participatory structures. Respondents made both short-term, feasible recommendations and more systemic, ideational recommendations to improve vulnerable women's participation. Integrating the vulnerable women's creative and feasible ideas to enhance their participation in health-system decision-making should be prioritized.
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.010 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".