Health research priority setting in Zambia: a stock taking of approaches conducted from 1998 to 2015
Bibliographic record
Abstract
BACKGROUND: Priority setting in health research is an emerging field. In Zambia, like many other African countries, various priority setting activities have been undertaken with a view to identify research activities to which the available resources can be targeted while at the same time maximising the health impact for resource allocation to support evidence-based decision-making. The aim of this paper is to document the key elements of the various priority setting activities that have been conducted since 1998, identifying the key lessons and providing recommendations to improve the process. METHODS: A comprehensive review of the previous priority setting activities and processes in Zambia was conducted. Both published and unpublished reports were reviewed in order to identify any research priority setting processes that have been undertaken in Zambia. We developed a framework, based on the priority setting literature, to guide our abstraction and synthesis of the literature. RESULT: The earliest record of priority setting was conducted in 1998. Various priority setting approaches have been implemented in Zambia; ranging from externally driven, once-off activities to locally (in country) initiated comprehensive processes. However, there has been no systematic national process for priority setting. These priority setting processes in Zambia were characterised by limited stakeholder buy-in of the resulting national research or programmatic research agenda. Most striking was the lack of linkages between the different initiatives. There seems to have been no conscious recognition and building on previous priority-setting experiences of previous initiatives. CONCLUSION: There were gaps in the priority setting processes, stakeholder engagement and application of a defined criterion. There is a need for a priority setting framework coupled with local capacity developed across a range of stakeholders.
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 | 0.180 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".