Developing policy-ready digital dashboards of geospatial access to emergency obstetric care: a survey of policymakers and researchers in sub-Saharan Africa
Bibliographic record
Abstract
Abstract Background Dashboards are increasingly being used in sub-Saharan Africa (SSA) to support health policymaking and governance. However, their use has been mostly limited to routine care, not emergency services like emergency obstetric care (EmOC). To ensure a fit-for-purpose dashboard, we conducted an online survey with policymakers and researchers to understand key considerations needed for developing a policy-ready dashboard of geospatial access to EmOC in SSA. Methods Questionnaires targeting both stakeholder groups were pre-tested and disseminated in English, French, and Portuguese across SSA. We collected data on participants’ awareness of concern areas for geographic accessibility of EmOC and existing technological resources used for planning of EmOC services, the dynamic dashboard features preferences, and the dashboard's potential to tackle lack of geographic access to EmOC. Questions were asked as multiple-choice, Likert-scale, or open-ended. Descriptive statistics were used to summarise findings using frequencies or proportions. Free-text responses were recoded into themes where applicable. Results Among the 206 participants (88 policymakers and 118 researchers), 90% reported that rural areas and 23% that urban areas in their countries were affected by issues of geographic accessibility to EmOC. Five percent of policymakers and 38% of researchers were aware of the use of maps of EmOC facilities to guide planning of EmOC facility location. Regarding dashboard design, most visual components such as location of EmOC facilities had almost universal desirability; however, there were some exceptions. Nearly 70% of policymakers considered the socio-economic status of the population and households relevant to the dashboard. The desirability for a heatmap showing travel time to care was lower among policymakers (53%) than researchers (72%). Nearly 90% of participants considered three to four data updates per year or less frequent updates adequate for the dashboard. The potential usability of a dynamic dashboard was high amongst both policymakers (60%) and researchers (82%). Conclusion This study provides key considerations for developing a policy-ready dashboard for EmOC geographical accessibility in SSA. Efforts should now be targeted at establishing robust estimation of geographical accessibility metrics, integrated with existing health system data, and developing and maintaining the dashboard with up-to-date data to maximise impact in these settings.
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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 | 0.044 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".