Barriers and facilitators to healthcare facility utilization by non-Ebola patients during the 2018–2020 Ebola outbreak in the Democratic Republic of Congo
Notice bibliographique
Résumé
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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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
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