What are the trends in seeking health care for fever in children under-five in Sierra Leone? evidence from four population-based studies before and after the free health care initiative
Bibliographic record
Abstract
BACKGROUND: In 2010, the government of Sierra Leone implemented the Free Health Care Initiative (FHCI) in the country with the objective of reducing the high maternal, infant, and child mortality rates and improving general health indicators. The objective of this study was to assess the trends in the prevalence of health care-seeking and to identify the determinants of healthcare service utilization by caregivers of children younger than five years. METHODS: The analysis of health-care-seeking behavior was done using data from four population-based surveys in Sierra Leone before (2008) and after (2013, 2016, 2019) the FHCI was implemented. Care-seeking behavior was assessed with regard to caregivers seeking care for children under-five in the two weeks prior to each survey. We compared the percentages of healthcare-seeking behavior change and identify factors associated with healthcare-seeking using a modified Poisson regression model with generalized estimating equations. RESULTS: In 2008, a total of 1208 children with fever were recorded, compared with 2823 children in 2013, 1633 in 2016, and 1464 in 2019. Care-seeking for children with fever was lowest in 2008 (51%; 95% CI (46.4-55.5)) than in 2013 (71.5%; 95% CI (68.4-74.5)), 2016 (70.3%; 95% CI (66.6-73.8)), and 2019 (74.6%; 95% CI (71.6-77.3)) (p < 0.001). Care-seeking in 2013, 2016 and 2019 was at least 1.4 time higher than in 2008 (p < 0.001) after adjusting for mother's age, wealth, religion, education level, household head and the child's age. Care-seeking was lowest for children older than 12 months, mothers older than 35 years, children living in the poorest households, and in the northern region. A trend was observed for the sex of the household head. The level of care-seeking was lowest when the household head was a man. CONCLUSIONS: The increase in healthcare-seeking for children under-five with fever followed the introduction of the FHCI in Sierra Leone. Care-seeking for fever varied by the child's age, caregiver's age, household wealth, the sex of the household head and region. Maintaining the FHCI with adequate strategies to address other barriers beyond financial ones is essential to reduce disparities between age groups, regions and, households.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".