Effect of On-Site Support on Laboratory Practice for Human Immunodeficiency Virus, Tuberculosis, and Malaria Testing
Bibliographic record
Abstract
OBJECTIVES: To evaluate the effect of on-site support in improving human immunodeficiency virus (HIV) rapid testing, tuberculosis (TB) sputum microscopy, and malaria microscopy among laboratory staff in a low-resource setting. METHODS: This cluster randomized trial was conducted at 36 health facilities in Uganda. From April to December 2010, laboratory staff at 18 facilities participated in monthly on-site visits, and 18 served as control facilities. After intervention, 128 laboratory staff were observed performing 587 laboratory tests across three diseases: HIV rapid testing, TB sputum microscopy, and malaria microscopy. Outcomes were the proportion of laboratory procedures correctly completed for the three laboratory tests. RESULTS: Laboratory staff in the intervention arm performed significantly better than the control arm in correctly completing laboratory procedures for all three laboratory tests, with an adjusted relative risk (95% confidence interval) of 1.18 (1.10-1.26) for HIV rapid testing, 1.29 (1.21-1.40) for TB sputum microscopy, and 1.19 (1.11-1.27) for malaria microscopy. CONCLUSIONS: On-site support significantly improved laboratory practices in conducting HIV rapid testing, TB sputum microscopy, and malaria microscopy. It could be an effective method for improving laboratory practice, without taking limited laboratory staff away from health facilities for training.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".