“Children get sick all the time”: A qualitative study of socio-cultural and health system factors contributing to recurrent child illnesses in rural Burkina Faso
Bibliographic record
Abstract
BACKGROUND: In Burkina Faso, the government has implemented various health sector reforms in order to overcome financial and geographical barriers to citizens' access to primary healthcare throughout the country. Despite these efforts, morbidity and mortality rates among children remain high and the utilization of public healthcare services low. This study explores the relationship between mothers' intentions to use public health services in cases of child sickness, their social strategies and cultural practices to act on these intentions and the actual services provided at the primary health care facilities. Focusing on mothers as the primary caregivers, we follow their pathways from the onset of symptoms through their various attempts of providing treatment for their sick children. The overall objective is to discuss the interconnectedness of various factors, inside and outside of the primary health care services that contribute to the continuing high child morbidity and mortality rates. METHODS: The study is based on ethnographic fieldwork, including in-depth interviews and follow-up interviews with 27 mothers, informal observations of daily-life activities and structured observations of clinical encounters. Data analysis took the form of thematic analysis. RESULTS AND DISCUSSION: Focusing on the mothers' social strategies and cultural practices, three forms of responses/actions have been identified: home-treatment, consultation with a traditional specialist, and consultation at the primary health care services. Due to their accumulated vulnerabilities, mothers shift pragmatically from one treatment to another. However, the sporadic nature of their treatment-seeking hinders them in obtaining long-term solutions and the result is recurrent child illnesses and relapses over long periods of time. The routinization of the clinical encounter at rural dispensaries furthermore fails to address these complexities of children's illnesses. CONCLUSIONS: The analysis of case studies, interviews and observations shows how mothers in a rural area struggle and often fail to receive care at public healthcare facilities. Health service delivery could be organized in a manner that responds better to the needs of these mothers in terms of both access and retention.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".