Country Learning on Maintaining Quality Essential Health Services (EHS) during COVID-19 in Timor-Leste: A mixed methods qualitative analysis
Notice bibliographique
Résumé
ABSTRACT Objective This research study examines the enabling factors, strengths, and challenges experienced by the Timor-Leste health system as it sought to maintain quality essential health services (EHS) during the COVID-19 pandemic. Design A mixed methods qualitative analysis Setting National, municipal, facility levels in Baucau, Dili and Ermera Municipalities in TLS Participants Key informant interviews (n=40) and focus group discussions (n=6) working to maintain quality EHS in TLS. Results A reduction in people accessing general health services was observed in 2020, reportedly due to fears of contracting COVID-19 in healthcare settings, limited resources (eg. human resources, personal protective equipment, clinical facilities, etc) and closure of health services. However, improvements in maternal child health services simultaneously improved in the areas of skilled birth attendants, prenatal coverage, and vitamin A distribution, for example. Five themes emerged as enabling factors for maintaining quality EHS including 1) high level strategy for maintaining quality EHS, 2) implementation of quality activities across the three levels of the health system, 3) measurement for quality and factors affecting service utilization 4) the positive impact of quality improvement leadership in health facilities during COVID-19, and 5) learning from each other for maintaining quality EHS now and for the future. Other countries may benefit from the challenges, strengths and enablers found on planning for quality. Conclusion The maintenance of quality essential health services (EHS) is critical to mitigate adverse health effects from the COVID-19 pandemic. When quality health services are delivered prior to and maintained during public health emergencies, they build trust within the health system and promote healthcare seeking behavior. Planning for quality as part of emergency preparedness can facilitate a high standard of care by ensuring health services continue to provide a safe environment, reduce harm, improve clinical care, and engage patients, facilities, and communities. DATA SHARING All data is kept with MBK and GR and is available upon request. The dataset analysis is available from the corresponding author upon reasonable request. QUALITATIVE CHECKLIST The Standards for Reporting Qualitative Research (SRQR) checklist was used for this original research. STRENGTHS AND LIMITATIONS OF THIS STUDY The qualitative data gave detailed insights to the operationalization of key strategic COVID-19 emergency documents and the national quality implementation strategy. Data collection was performed in three out of thirteen municipalities, including the largest metropolitan city of Dili. The qualitative research was conducted in the participants native language (Tetum). Not all pre-identified national level KII participants were available to provide feedback.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,014 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,006 | 0,005 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,001 | 0,006 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 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 source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».