The Impact on Absence from School of Rapid Diagnostic Testing and Treatment for Malaria by Teachers.
Bibliographic record
Abstract
Malaria is the principal preventable reason a child misses school in sub-Saharan Africa and the leading cause of death in school-aged children We describe a model for teachers to use rapid diagnostic testing (RDT) for malaria and treatment with Artemisinin-based combination therapy (ACT) to enhance education by reducing school absence due to malaria. Conduct: A 2 year pilot program in 4 primary schools in rural Uganda. Year 1, Pre-intervention baseline evaluation (malaria knowledge; school practices when pupils become sick; monitoring of days absent as a surrogate for morbidity and teachers trained to administer RDT/ACT as the Year 2 intervention. Findings: Teachers identified malaria as a barrier to education, contributed to logistic design, participated willingly, collected accurate data, and readily implemented/sustained RDT/ACT program. Pre-intervention: 953/1764 pupils were sent home due to presumed infectious illness; mean duration of absence was 6.5 days (SD: 3.17). With school-based teacher-administered RDT/ACT 1066/1774 pupils were identified as sick, 765/1066 (67.5%) tested RDT positive for malaria and received ACT; their duration of absence fell to 0.59 days (SD: 0.64) (p<0.001) and overall absenteeism to 2.55 (SD: 3.35). This RDT/ACT program empowered teachers, significantly reduced days of education lost due to malaria and is applicable to other schools.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".