Governance for injury care systems in Ghana, South Africa and Rwanda: development and pilot testing of an assessment tool
Bibliographic record
Abstract
OBJECTIVES: This study aims to evaluate health systems governance for injury care in three sub-Saharan countries from policymakers' and injury care providers' perspectives. SETTING: Ghana, Rwanda and South Africa. DESIGN: 's framework for governance, we developed an online assessment tool for health system governance for injury with 37 questions covering health policy and implementation under 10 overarching principles of strategic vision, participation and consensus orientation, rule of law, transparency, responsiveness of institutions, equity, effectiveness or efficiency, accountability, ethics and intelligence and information. A literature review was also done to support the scoring. We derived scores using two methods-investigator scores and respondent scores. PARTICIPANTS: The tool was sent out to purposively selected stakeholders, including policymakers and injury care providers in Ghana, Rwanda and South Africa. Data were collected between October 2020 and February 2021. PRIMARY AND SECONDARY OUTCOMES: Investigator-weighted and respondent percentage scores for health system governance for injury care. This was calculated for each country in total and per principle. RESULTS: Rwanda had the highest overall investigator-weighted percentage score (70%), followed by South Africa (59%). Ghana had the lowest overall investigator score (48%). The overall results were similar for the respondent scores. Some areas, such as participation and consensus, scored high in all three countries, while other areas, such as transparency, scored very low. CONCLUSION: In this multicountry governance survey, we provide insight into and evaluation of health system governance for trauma in three low- and middle-income countries (LMICs) in sub-Saharan Africa. It highlights areas of improvement that need to be prioritised, such as transparency, to meet the high burden of trauma and injuries in LMICs.
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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 | 0.027 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".