Methodological Assessment of Quasi-Experimental Designs in South African District Hospitals: A Scoping Review
Bibliographic record
Abstract
Quasi-experimental designs are commonly used in healthcare research to evaluate interventions without random assignment but with controlled conditions. In South African district hospitals, these designs have been employed to assess clinical outcomes following various health programmes. A comprehensive search strategy was conducted using databases such as PubMed, Embase, and Cochrane Library. Eligible studies were identified based on predefined inclusion criteria and assessed for methodological quality using the Newcastle-Ottawa Scale (NOS). The review identified a total of 25 quasi-experimental designs applied in South African district hospitals over the past decade, primarily targeting interventions related to maternal health and child nutrition. Analysis revealed significant variability in study design quality. Findings suggest that while these studies have provided valuable insights into healthcare outcomes, there is room for improvement in methodological consistency and transparency. Future research should prioritise standardisation of quasi-experimental designs to enhance comparability and generalizability across different contexts. Increased reporting of study methods will also improve the reliability of findings. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.
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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 | 0.585 | 0.750 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.013 | 0.012 |
| Bibliometrics | 0.021 | 0.021 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".