Testing a simplified tool and training package to improve integrated Community Case Management in Tanganyika Province, Democratic Republic of Congo: a quasi-experimental study
Bibliographic record
Abstract
Testing a simplified tool and training package to improve integrated Community Case Management in Tanganyika Province, Democratic Republic of Congo: a quasi-experimental study Background Integrated community case management (iCCM) is a strategy to train community health workers (relais communautaires or RECOs in French) in low-resource settings to provide treatment for uncomplicated malaria, pneumonia, and diarrhea for children 2-59 months of age.The package of Ministry of Public Health tools for RECOs in the Democratic Republic of Congo that was being used in 2013 included seven data collection tools and job aids which were redundant and difficult to use.As part of the WHO-supported iCCM program, the International Rescue Committee developed and evaluated a simplified set of pictorial tools and curriculum adapted for low-literate RECOs. MethodsThe revised training curriculum and tools were tested in a quasi-experimental study, with 74 RECOs enrolled in the control group and 78 RECOs in the intervention group.Three outcomes were assessed during the study period from Sept. 2015-July 2016: 1) quality of care, measured by direct observation and reexamination; 2) workload, measured as the time required for each assessment -including documentation; and 3) costs of rolling out each package.Logistic regression was used to calculate odds ratios for correct treatment by the intervention group compared to the control group, controlling for characteristics of the RECOs, the child, and the catchment area. ResultsChildren seen by the RECOs in the intervention group had nearly three times higher odds of receiving correct treatment (adjusted odds ratio aOR = 2.9, 95% confidence interval CI = 1.3-6.3,P = 0.010).On average, the time spent by the intervention group was 10.6 minutes less (95% CI = 6.6-14.7,P < 0.001), representing 6.2 hours of time saved per month for a RECO seeing 35 children.The estimated cost savings amounts to over US$ 300 000 for a four-year program supporting 1500 RECOs. ConclusionThis study demonstrates that, at scale, simplified tools and a training package adapted for low-literate RECOs could substantially improve health outcomes for under-five children while reducing implementation costs and decreasing their workload.The training curriculum and simplified tools have been adopted nationally based on the results from this study.
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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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".