Barriers and facilitators to healthcare facility utilization by non-Ebola patients during the 2018–2020 Ebola outbreak in the Democratic Republic of Congo
Bibliographic record
Abstract
BACKGROUND: An Ebola Virus Disease (EVD) outbreak occurred in North Kivu between 2018 and 2020. This eastern province of the Democratic Republic of Congo was also grappling with insecurity caused by several armed groups. This study aimed to explore the barriers and facilitators to utilizing Healthcare Facilities (HCFs) by non-Ebola patients during the crisis. METHODS: A qualitative case study was conducted in Beni and Butembo with 24 relatives of 15 deceased non-EVD patients, 47 key informants from healthcare workers (HCWs), as well as community leaders. Semi-structured interviews were conducted to explore three key areas: (i) the participants' illness history, care pathway, care, and social support; (ii) their perceptions of how EVD affected the care outcome; and (iii) their opinions on the preparedness, supply, use, and quality of healthcare before and during the outbreak. All interviews were recorded, transcribed verbatim, and thematically analysed using Atlas-ti 8.0. RESULTS: Nine of the 15 deaths were female and their ages ranged from 7 to 79 years. The causes of death were non-communicable (13) or infectious (2) diseases. Conspiracy theories, failure to establish security, and the concept of the ''Ebola business'' were associated with misinformation and lower levels of trust in government and HCFs. The negative perceptions, fear of being identified as an Ebola case, apprehension about the triage unit, and inadequacy of personal protective equipment resulted in a preference for private or informal HCFs. For half of the deceased's relatives, the Ebola outbreak hastened their death. Conversely, community involvement, employing familiar, neutral, and credible HCWs, and implementing a free care policy increased the number of visits. These results were observable despite a lack of funds, overstretched HCWs, and long waiting time. CONCLUSIONS: Our findings can inform policies before and during future outbreaks to enhance the resilience of routine HCFs by maintaining dialogue between HCWs and patients, and rebuilding confidence in HCFs. Quantitative studies including context analysis are essential to identify the determinants of care-seeking during such a crisis.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".