A cross-sectional study to identify the distribution and characteristics of licensed and unlicensed private drug shops in rural Eastern Uganda to inform an iCCM intervention to improve health outcomes for children under five years
Bibliographic record
Abstract
INTRODUCTION: Malaria, pneumonia and diarrhea are leading causes of death in young children in Uganda. Between 50-60% of sick children receive treatment from the private sector, especially drug shops. There is an urgent need to improve quality of care and regulation of private drug shops in Uganda. This study was conducted to determine the distribution, the licensing status and characteristics of drug shops in four sub-districts of Kamuli district. METHODS: This study was part of a pre-post cross sectional study that examined the implementation of an integrated Community Case Management (iCCM) intervention for common childhood illness in rural private drug shops in Kamuli District in Eastern Uganda. This mapping exercise used a snowball sampling technique to identify licensed and unlicensed drug shops and collect information about their characteristics. Data were collected using a questionnaire. GPS data were collected for all drug shops. ANALYSIS: Quantitative data were analyzed using SPSS for descriptive statistics. Open ended questions were entered into NVivo 10 and analyzed using thematic analysis strategies. RESULTS: In total, 215 drug shops in 284 villages were located. Of these, 123 (57%) were open and consented to an interview. Only 12 (10%) drug shops were licensed, 93 (76%) were unlicensed, and the licensing status of 18 (15%) was unknown. Most respondents were the owner of the drug shop (88%); most drug sellers reported their qualification as nursing assistants (70%). Drug sellers reported licensing fees and costs of contracting an "in-charge" as barriers to licensing. Nearly all drug shops sold drugs for malaria (91%) and antibiotics (79%).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".