The unfunded priorities: an evaluation of priority setting for noncommunicable disease control in Uganda
Bibliographic record
Abstract
BACKGROUND: The double burden of infectious diseases coupled with noncommunicable diseases poses unique challenges for priority setting and for achieving equitable action to address the major causes of disease burden in health systems already impacted by limited resources. Noncommunicable disease control is an important global health and development priority. However, there are challenges for translating this global priority into local priorities and action. The aim of this study was to evaluate the influence of national, sub-national and global factors on priority setting for noncommunicable disease control in Uganda and examine the extent to which priority setting was successful. METHODS: A mixed methods design that used the Kapiriri & Martin framework for evaluating priority setting in low income countries. The evaluation period was 2005-2015. Data collection included a document review (policy documents (n = 19); meeting minutes (n = 28)), media analysis (n = 114) and stakeholder interviews (n = 9). Data were analysed according to the Kapiriri & Martin (2010) framework. RESULTS: Priority setting for noncommunicable diseases was not entirely fair nor successful. While there were explicit processes that incorporated relevant criteria, evidence and wide stakeholder involvement, these criteria were not used systematically or consistently in the contemplation of noncommunicable diseases. There were insufficient resources for noncommunicable diseases, despite being a priority area. There were weaknesses in the priority setting institutions, and insufficient mechanisms to ensure accountability for decision-making. Priority setting was influenced by the priorities of major stakeholders (i.e. development assistance partners) which were not always aligned with national priorities. There were major delays in the implementation of noncommunicable disease-related priorities and in many cases, a failure to implement. CONCLUSIONS: This evaluation revealed the challenges that low income countries are grappling with in prioritizing noncommunicable diseases in the context of a double disease burden with limited resources. Strengthening local capacity for priority setting would help to support the development of sustainable and implementable noncommunicable disease-related priorities. Global support (i.e. aid) to low income countries for noncommunicable diseases must also catch up to align with NCDs as a global health priority.
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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 | 0.262 | 0.345 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| 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".