Measuring hospital performances of regional referral hospitals in Tanzania
Bibliographic record
Abstract
In Tanzania, regional referral hospitals (RRHs) play a major role in providing curative and diagnostic services and influence the performance of the entire health system. The results of a baseline survey conducted in 2015 to determine the status of RRHs in Tanzania indicated that there were many supportive supervisory and assessment tools for RRHs but none of them specifically focused on the performance of hospitals. In an endeavor to enhance the performance of RRHs, the Ministry of Health, Community Development, Gender, Elderly and Children (MoHCDGEC) and the President Office – Regional Administration and Local Government (PO-RALG) developed an external hospital performance assessment (EHPA) tool to analyze all aspects of the performance of RRHs. EHPA was started in 2017 to assess the performance of 28 RRHs in the country. This study examines the changes to the performance of the RRHs based on the introduction of the EHPA and the supportive interventions by the Ministry of Health. It is also studying the factors that influence the assessment of EHPA. As the results of this study, there is a great indication of the overall performance of RRHs being improved as observed from an upward gradient of average EHPA scores from 2017 to 2019 in all RRHs. This improvement is exemplified by the decrease in the standard deviation gap amongst RRHs. The three years (2017–2019) of consecutive assessment has also observed implicit competition in improving hospital services among Regional Referral Hospital Management Teams (RRHMTs) using the findings from the EHPA.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".