Effect of shortened Integrated Management of Childhood Illness training on classification and treatment of under-five children seeking care in Rwanda
Bibliographic record
Abstract
BACKGROUND: Integrated Management of Childhood Illness (IMCI) is an effective 11-day standard training; however, due to budgetary expenses and human resource constraints, many health professionals cannot take 11 days off work. As a result, shortened training curriculums (6-day) have been proposed. We used a cross-sectional study to evaluate the effect of this shortened training on appropriate IMCI classification and treatment of under-five childhood illness management in Rwanda. METHODS: A cross-sectional study was conducted in 22 health centers in Rwanda, comparing data from 121 nurses, where 55 nurses completed the 11-day and 66 nurses completed the 6-day training. Among 768 children, we evaluated clinical outcomes from May 2011 to April 2012. Descriptive statistics were used to display the sociodemographic characteristics of health providers; including level of education, sex, age, and professional experiences. Bivariable and multivariable analyses were used to test for differences between nurses in the 6-day versus 11-day training on the appropriate classification and treatment of childhood illness. RESULTS: Our findings show that at the bivariable level and after controlling for confounders in the multivariable analysis, the only significant differences detected between nurses in the long and short training was the classification of fever (adjusted odds ratio [aOR] 0.7, 95% confidence interval [CI] 0.64-0.75) and treatment of pneumonia (aOR 0.8, 95% CI 0.70-0.89). Nurses in the short training had lower odds of inappropriate misclassification and treatment for these two conditions. CONCLUSION: There was no difference in classification and treatment of childhood illness among nurses who completed the standard and short IMCI training courses. Short-training could be a more cost-saving option for health facilities without compromising the key outcomes related to case management.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".