Treatment-seeking and uptake of malaria prevention strategies among pregnant women and caregivers of children under-five years during COVID-19 pandemic in rural communities in South West Uganda: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Despite efforts to avert the negative effects of malaria, there remain barriers to the uptake of prevention measures, and these have hindered its eradication. This study explored the factors that influence uptake of malaria prevention strategies among pregnant women and children under-five years and the impact of COVID-19 in a malaria endemic rural district in Uganda. METHODS: This was a qualitative case study that used focus group discussions, in-depth interviews, and key informant interviews involving pregnant women, caregivers of children under-five years, traditional birth attendants, village health teams, local leaders, and healthcare providers to explore malaria prevention uptake among pregnant women and children under-five years. The interviews were audio-recorded, transcribed and data were analyzed using thematic content approach. RESULTS: Seventy-two participants were enrolled in the Focus Group Discussions, 12 in the in-depth interviews, and 2 as key informants. Pregnant women and caregivers of children under-five years were able to recognize causes of malaria, transmission, and symptoms. All participants viewed malaria prevention as a high priority, and the use of insecticide-treated mosquito bed nets (ITNs) was upheld. Participants' own experiences indicated adverse effects of malaria to both pregnant women and children under-five. Home medication and the use of local herbs were a common practice. Some participants didn't use any of the malaria prevention methods due to deliberate refusal, perceived negative effects of the ITNs, and family disparity. The Corona Virus Disease-2019 (COVID-19) control measures did not abate the risk of malaria infection but these were deleterious to healthcare access and the focus of malaria prevention. CONCLUSIONS: Although pregnant women and caregivers of children under-five years recognized symptoms of malaria infection, healthcare-seeking was not apt as some respondents used alternative approaches and delayed seeking formal healthcare. It is imperative to focus on the promotion of malaria prevention strategies and address drawbacks associated with misconceptions about these interventions, and promotion of health-seeking behaviors. As COVID-19 exacerbated the effect of malaria prevention uptake and healthcare seeking, it's critical to recommit and integrate COVID-19 prevention measures in normative living and restrict future barriers to healthcare access.
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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 | 0.002 | 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".