Development and Formative Evaluation of a Visual E-Tool to Help Decision Makers Navigate the Evidence Around Health Financing
Bibliographic record
Abstract
BACKGROUND: There are calls for low and middle income countries to develop robust health financing policies to increase service coverage. However, existing evidence around financing options is complex and often difficult for policy makers to access. OBJECTIVE: To summarize the evidence on the impact of financing health systems and develop an e-tool to help decision makers navigate the findings. METHODS: After reviewing the literature, we used thematic analysis to summarize the impact of 7 common health financing mechanisms on 5 common health system goals. Information on the relevance of each study to a user's context was provided by 11 country indicators. A Web-based e-tool was then developed to assist users in navigating the literature review. This tool was evaluated using feedback from early users, collected using an online survey and in-depth interviews with key informants. RESULTS: The e-tool provides graphical summaries that allow a user to assess the following parameters with a single snapshot: the number of relevant studies available in the literature, the heterogeneity of evidence, where key evidence is lacking, and how closely the evidence matches their own context. Users particularly liked the visual display and found navigating the tool intuitive. However there was concern that a lack of evidence on positive impact might be construed as evidence against a financing option and that the tool might over-simplify the available financing options. CONCLUSIONS: Complex evidence can be made more easily accessible and potentially more understandable using basic Web-based technology and innovative graphical representations that match findings to the users' goals and context.
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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 | 0.145 | 0.350 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".