Improving access to appropriate case management for common childhood illnesses in hard-to-reach areas of Abia State, Nigeria
Bibliographic record
Abstract
# Background Studies have demonstrated that trained community health workers can improve access to quality health services for under five children. Under the World Health Organization's Rapid Access Expansion Progamme, integrated community case management of childhood illnesses (iCCM) was introduced in Abia and Niger States, Nigeria in 2013. The objective of the program was to increase the number of children 2-59 months receiving quality life-saving treatment for malaria, pneumonia and diarrhoea by extending case management through community-oriented resource persons (CORPs). We present findings from household surveys comparing baseline and endline data to assess changes in sick child care-seeking, assessment, and treatment coverage provided over the project period in Abia State. # Methods A baseline household survey was conducted in May 2014 and an endline survey in February 2017. The surveys used multi-stage cluster sampling of primary caregivers of children aged 2-59 months who had been recently sick with diarrhoea, fever, or cough with difficult breathing. # Results Care-seeking from an appropriate provider improved significantly from 69% at baseline to 77% at endline (*P*\<0.01). At baseline, patent and proprietary medicine vendors (PPMVs) (55%) and health centers (34%) were the main providers of care for iCCM services; by endline, CORPs became the main source (48%), followed by PPMVs (36%) and health centers (25%). # Conclusions Overall, the findings demonstrate improvements in care-seeking. Care-seeking practices shifted over the course of the project, with more caregivers seeking care from CORPs by the end of the project. The findings suggest that scaling up iCCM in Nigeria may improve access to appropriate treatment for under five children living in hard-to-reach areas.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".