A qualitative exploration of care-seeking pathways for sick children in the rural Oromia region of Ethiopia
Bibliographic record
Abstract
BACKGROUND: Ethiopia has experienced rapid improvements in its healthcare infrastructure, such as through the recent scale up of integrated community case management (iCCM) delivered by community-based health extension workers (HEWs) targeting children under the age of five. Despite notable improvements in child outcomes, the use of HEWs delivering iCCM remains very low. The aim of our study was to explain this phenomenon by examining care-seeking practices and treatment for sick children in two rural districts in the Oromia Region of Ethiopia. METHODS: Using qualitative methods, we explored perceptions of child illness, influences on decision-making processes occurring over the course of a child's illness and caregiver perceptions of available community-based sources of child illness care. Sixteen focus group discussions (FGDs) and 40 in-depth interviews (IDIs) were held with mothers of children under age five. For additional perspective, 16 IDIs were conducted fathers and 22 IDIs with health extension workers and community health volunteers. RESULTS: Caregivers often described the act of care-seeking for a sick child as a time of considerable uncertainty. In particular, mothers of sick children described the cultural, social and community-based resources available to minimize this uncertainty as well as constraints and strategies for accessing these resources in order to receive treatment for a sick child. The level of trust and familiarity were the most common dynamics noted as influencing care-seeking strategies; trust in biomedical and government providers was often low. CONCLUSIONS: Overall, our research highlights the multiple and dynamic influences on care-seeking for sick children in rural Ethiopia. An understanding of these influences is critical for the success of existing and future health interventions and continued improvement of child health in Ethiopia.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".