Long and Short Integrated Management of Childhood Illness (IMCI) Training Courses in Afghanistan: A Cross-Sectional Cohort Comparison of Post-Course Knowledge and Performance
Bibliographic record
Abstract
Background: In 2003 the Afghan Ministry of Public Health (MoPH) adopted the Integrated Management of Childhood \nIllness (IMCI) for delivering child health services in primary care facilities. Key problems were subsequently identified: \nhigh cost of training, frequent health worker turnover and poor quality of IMCI implementation by those trained \n– specifically in the use of job aids and protocols for assessment, classification, treatment and counselling. The high \nfinancial, human resources and opportunity costs of implementing IMCI spurred the MoPH to prioritize developing a \nshortened IMCI course of comparable quality to the 11-Day training. \nMethods:This cross-sectional evaluation compared knowledge before and after training, and health worker performance \nin assessment, classification and treatment of sick children in two similar cohorts, eight months post-training. \nResults:The mean increase in knowledge scores of the thirty 7-Day course trainees was 29 [95% Confidence Interval \n(CI): 24, 34] compared to 23 (95% CI: 18, 28) in the 31 trained in the 11-Day course. During assessment visits, mean \nscores in the 7-Day course trainees and the 11-Day course trainees were 93% (95% CI: 91, 95) versus 94% (95% CI: 91, 96) \nin assessment; 95% (95% CI: 89, 100) versus 96% (95% CI: 91, 100) in classification; 95% (95% CI: 92, 100) versus 97% \n(95% CI: 95, 100) in treatment; and 81% (95% CI: 76, 86) versus 80% (95% CI: 75, 85) in counselling. The 7-Day course \nwas 36% less expensive than the 11-Day course. For each course opportunity costs, measured as numbers of children \nwho potentially received poorer care than usual during trainee absence, were 3,160 for the 11-Day course and 2,016 for \nthe 7-Day course. This measure was chosen because trainee absence commonly resulted in higher patient volumes per \nremaining provider or complete closure of a health facility with one single health worker. \nConclusion:Given similar performance and knowledge of health workers trained in both courses, potential cost savings, \nthe possibility of training more health workers and the relative ease with which health workers in remote settings might \nparticipate in a shorter course, it seems prudent to standardize the 7-Day course in Afghanistan where child mortality \nrates remain unacceptably high.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".