Identifying Key Steps in Developing a One-stop Shop for Health Policy and System Information in a Limited-resource Setting: A Case Study
Bibliographic record
Abstract
Background: Limited understanding exists about the development of online one-stop shops for evidence in a limited-resource setting, such as Uganda. This study aimed to provide a comprehensive account of the development process of the online resource for local policy and systems-relevant information in this setting. Methods: We utilized a case study design to address our objective where the case (i.e., unit of analysis) was defined as “the Uganda clearinghouse for health policy and system (UCHPS) the development process”. We collected data from multiple sources, including key informant interviews, participant observations, and archival records to develop a comprehensive account of the case under investigation. Results: We found out that the development of Uganda clearinghouse for health policy and system (UCHPS) followed a seven-step process, characterized by iterations that occurred within and between the steps. The essential components of the process included concept development, prototyping the key structure, engaging with policymakers, researchers, and other stakeholders, mobilizing and indexing the content, disseminating the resource, user-testing, and updating the system. Conclusion: Our study provides key steps for developing a one-stop shop for local evidence to inform health policy and system decisions. Researchers and institutions, especially those in low and middle income countries (LMICs) may apply this step-by-step inventory to develop similar resources. The inventory is based on knowledge translation (KT) evidence and product design principles along with insights drawn from the practical experience of developing an online KT platform in a limited-resource setting.
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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 | 0.066 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.017 | 0.015 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".