Methodological Evaluation of District Hospitals in Rwanda: A Systematic Review of Clinical Outcomes Measurement Frameworks
Bibliographic record
Abstract
Rwanda's district hospitals play a crucial role in primary healthcare delivery across the country. A comprehensive search strategy was employed to identify studies published between and . Studies were screened based on predefined inclusion criteria, assessed for quality using the Newcastle-Ottawa Scale (NOS), and analysed according to a standardised data extraction protocol. The review identified a prevalence of mixed-methods evaluation frameworks in Rwanda's district hospitals, with some studies employing multivariate regression models to assess clinical outcomes. However, there is variability in the reporting of effect sizes and confidence intervals for these analyses. While existing frameworks show promise, they often lack standardization across different districts, leading to inconsistencies in measurement methodologies and interpretations. Standardising clinical outcome measurement frameworks would enhance comparability and reliability of data across Rwanda's district hospitals. This could facilitate more robust policy recommendations for improving healthcare delivery. clinical outcomes, district hospitals, evaluation framework, Rwanda Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.215 | 0.433 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.025 | 0.027 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".