A protocol for engaging unlicensed private drug shops in Rural Eastern Uganda for Integrated Community Case Management (ICCM) of malaria, pneumonia and diarrhoea in children under 5 years of age
Bibliographic record
Abstract
INTRODUCTION: Malaria, pneumonia and diarrhoea are leading causes of death in young children in Uganda. In 2010, Integrated Community Case Management (ICCM) was adopted in Uganda for community level diagnosis and treatment of these diseases through community health workers. However, 50-60% of sick children will receive treatment from the private sector, especially drug shops. Only about half of drug shops are licensed and the quality of care is poor. There is an urgent need to improve quality of care and regulation of drug shops in Uganda. METHODS: This is a pre-post cross-sectional study with before and after measurement in an intervention area in Kamujli district. A snowball mapping exercise, exit interviews, focus group discussions and interviews will be used. 25 randomly selected drug shops will be selected for an intervention that will assist drug shops to become licensed, and provide five days of ICCM training, subsidised prepackaged medicines (artemisinin-based combination therapies for malaria, amoxicillin for pneumonia, Oral Rehydration Salts/zinc for diarrhoea) and free diagnostic tools (rapid diagnostic tests, respiratory timers, thermometers, algorithms). We anticipate a sample size of 1200 (600 at baseline and 600 at the end of the study). ANALYSIS: Quantitative data will be analysed using SPSS for proportions and CIs. Bivariate and multiple logistic regression analysis with adjustment for clustering of data will be performed to adjust for confounding and determine intervention effect. Qualitative data will be entered into NVivo 10 and analysed using content analysis. ETHICS AND DISSEMINATION: Research ethics approval is received from the University of Calgary (REB 14-0269), and Makerere University (IRB00011353). Findings from this study will be disseminated through journal articles and conference presentations, and will illustrate the feasibility of introducing ICCM for drug shops.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| 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".