Evaluating user experiences of a clearing house for health policy and systems
Bibliographic record
Abstract
BACKGROUND: Timely access to evidence increases the prospects for evidence informed decision making. We evaluated user experiences of a Clearing House for Health Policy with the aim of increasing access to evidence about the Uganda health system and interventions. METHODS: We conducted in-depth interviews with 15 potential users including policymakers, health policy advisors, health managers and researchers to provide evidence on their experience with the clearinghouse. On average participants took 20 minutes to first navigate the site and 45 minutes to perform search tasks and complete the interview. RESULTS: Most respondents successfully searched for information with accuracy and completeness in a short time. Participants commended the performance and expressed high regard for the credibility of the clearinghouse. The majority felt that using the resource was worth their effort. The clearinghouse provided appropriate functionalities for information searching. Navigating and finding information from the site was achievable. However, inadequate background information about the site and lack of current information were widely reported. CONCLUSION: Our paper provides insights on the issues that can be addressed to improve online resources for health policy and system information in a limited resource setting. Users' experience of such resources can be improved by regularly appraising and appropriately indexing the contents, and optimising the capacity to customise information.
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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 | 0.030 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".