Policymaker experiences with rapid response briefs to address health-system and technology questions in Uganda
Bibliographic record
Abstract
BACKGROUND: Health service and systems researchers have developed knowledge translation strategies to facilitate the use of reliable evidence for policy, including rapid response briefs as timely and responsive tools supporting decision making. However, little is known about users' experience with these newer formats for presenting evidence. We sought to explore Ugandan policymakers' experience with rapid response briefs in order to develop a format acceptable for policymakers. METHODS: We used existing research regarding evidence formats for policymakers to inform the initial version of rapid response brief format. We conducted user testing with healthcare policymakers at various levels of decision making in Uganda, employing a concurrent think-aloud method, collecting data on elements including usability, usefulness, understandability, desirability, credibility and value of the document. We modified the rapid response briefs format based on the results of the user testing and sought feedback on the new format. RESULTS: The participants generally found the format of the rapid response briefs usable, credible, desirable and of value. Participants expressed frustrations regarding several aspects of the document, including the absence of recommendations, lack of clarity about the type of document and its potential uses (especially for first time users), and a crowded front page. Participants offered conflicting feedback on preferred length of the briefs and use and placement of partner logos. Users had divided preferences for the older and newer formats. CONCLUSION: Although the rapid response briefs were generally found to be of value, there are major and minor frustrations impeding an optimal user experience. Areas requiring further research include how to address policymakers' expectations of recommendations in these briefs and their optimal length.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Scholarly communication Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | Scholarly communication Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
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 | 0.135 | 0.236 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".