Methodological Evaluation of Community Health Centres Systems in Ethiopia Using Difference-in-Differences Models
Bibliographic record
Abstract
Community health centers in Ethiopia have been established to improve access to healthcare services but their effectiveness remains under scrutiny. A difference-in-differences model will be employed to assess yield improvement in Ethiopia's community health centers, incorporating pre- and post-intervention data from control and treatment groups. A preliminary analysis suggests a positive trend in service delivery improvements with an estimated increase of 15% in healthcare services provided per quarter compared to baseline levels. The difference-in-differences model demonstrates the potential for quantifying yield improvement due to community health centre interventions, offering insights into their effectiveness. Further research should explore the specific factors contributing to service delivery improvements and evaluate long-term sustainability of these centers. Community Health Centers, Ethiopia, Difference-in-Differences, Healthcare Delivery 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 | 0.237 | 0.304 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".