Improving Facility Performance in Infectious Disease Care in Uganda: A Mixed Design Study with Pre/Post and Cluster Randomized Trial Components
Bibliographic record
Abstract
BACKGROUND: The effects of two interventions, Integrated Management of Infectious Disease (IMID) training program and On-Site Support (OSS), were tested on 23 facility performance indicators for emergency triage assessment and treatment (ETAT), malaria, pneumonia, tuberculosis, and HIV. METHODS: The trial was implemented in 36 primary care facilities in Uganda. From April 2010, two mid-level practitioners per facility participated in IMID training. Eighteen of 36 facilities were randomly assigned to Arm A, and received OSS in 2010 (nine monthly two-day sessions); 18 facilities assigned to Arm B did not receive OSS in 2010. Data were collected from Nov 2009 to Dec 2010 using a revised Ministry of Health outpatient medical form and nine registers. We analyzed the effect of IMID training alone by measuring changes before and during IMID training in Arm B, the combined effect of IMID training and OSS by measuring changes in Arm A, and the incremental effect of OSS by comparing changes across Arms A and B. RESULTS: IMID training was associated with statistically significant improvement in three indicators: outpatients triaged (adjusted relative risks (aRR) = 1.29, 99%CI = 1.01,1.64), emergency and priority patients admitted, detained, or referred (aRR = 1.59, 99%CI = 1.04,2.44), and pneumonia suspects assessed (aRR = 2.31, 99%CI = 1.50,3.55). IMID training and OSS combined was associated with improvements in six indicators: three ETAT indicators (outpatients triaged (aRR = 2.03, 99%CI = 1.13,3.64), emergency and priority patients admitted, detained or referred (aRR = 3.03, 99%CI = 1.40,6.56), and emergency patients receiving at least one appropriate treatment (aRR = 1.77, 99%CI = 1.10,2.84)); two malaria indicators (malaria cases receiving appropriate antimalarial (aRR = 1.50, 99%CI = 1.04,2.17), and patients with negative malaria test results prescribed antimalarial (aRR = 0.67, 99%CI = 0.46,0.97)); and enrollment in HIV care (aRR = 1.58, 99%CI = 1.32,1.89). OSS was associated with incremental improvement in emergency patients receiving at least one appropriate treatment (adjusted ratio of RR = 1.84,99%CI = 1.09,3.12). CONCLUSION: The trial showed that the OSS intervention significantly improved performance in one of 23 facility indicators.
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 | 0.024 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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; 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".