Does Integrated Management of Childhood Illness (IMCI) Training Improve the Skills of Health Workers? A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: An estimated 6.9 million children die annually in low and middle-income countries because of treatable illneses including pneumonia, diarrhea, and malaria. To reduce morbidity and mortality, the Integrated Management of Childhood Illness strategy was developed, which included a component to strengthen the skills of health workers in identifying and managing these conditions. A systematic review and meta-analysis were conducted to determine whether IMCI training actually improves performance. METHODS: Database searches of CIHAHL, CENTRAL, EMBASE, Global Health, Medline, Ovid Healthstar, and PubMed were performed from 1990 to February 2013, and supplemented with grey literature searches and reviews of bibliographies. Studies were included if they compared the performance of IMCI and non-IMCI health workers in illness classification, prescription of medications, vaccinations, and counseling on nutrition and admistration of oral therapies. Dersminion-Laird random effect models were used to summarize the effect estimates. RESULTS: The systematic review and meta-analysis included 46 and 26 studies, respectively. Four cluster-randomized controlled trials, seven pre-post studies, and 15 cross-sectional studies were included. Findings were heterogeneous across performance domains with evidence of effect modification by health worker performance at baseline. Overall, IMCI-trained workers were more likely to correctly classify illnesses (RR = 1.93, 95% CI: 1.66-2.24). Studies of workers with lower baseline performance showed greater improvements in prescribing medications (RR = 3.08, 95% CI: 2.04-4.66), vaccinating children (RR = 3.45, 95% CI: 1.49-8.01), and counseling families on adequate nutrition (RR = 10.12, 95% CI: 6.03-16.99) and administering oral therapies (RR = 3.76, 95% CI: 2.30-6.13). Trends toward greater training benefits were observed in studies that were conducted in lower resource settings and reported greater supervision. CONCLUSION: Findings suggest that IMCI training improves health worker performance. However, these estimates need to be interpreted cautiously given the observational nature of the studies and presence of heterogeneity.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.014 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".