Sociocultural and Behavioral Features of Anticipated COVID-19 Vaccine Acceptance in Papua New Guinea: Protocol for a Mixed Methods Study
Notice bibliographique
Résumé
BACKGROUND: COVID-19 was characterized by the World Health Organization (WHO) as a pandemic in 2020. Papua New Guinea (PNG) has remained on high alert ever since, and its National Control Centre continues to coordinate national preparedness and response measures, guided by its Emergency Preparedness and Response Plan for COVID-19. As part of the WHO and the Global Alliance for Vaccines and Immunization's COVID-19 Vaccines Global Access (COVAX) program, PNG received several shipments of COVID-19 vaccine doses. A nationwide vaccine rollout for COVID-19 was initiated in PNG in May 2021. Despite the availability of vaccines and the capacity of health systems to vaccinate frontline workers and community members, including high-risk groups, there are still critical issues related to vaccine safety, confidence, and acceptance to ensure the effectiveness of the COVID-19 vaccination campaign. Evidence from studies on COVID-19 vaccine acceptance and demand in low- and middle-income countries (LMICs) suggests that sociocultural characteristics of the community and the behaviors of different vaccine stakeholders, including vaccine recipients, vaccine providers, and policymakers, determine the effectiveness of vaccination interventions or strategies. OBJECTIVE: This study will examine sociocultural determinants of anticipated acceptance of the COVID-19 vaccine in urban and rural areas of different regions in PNG and health care providers' views on vaccine acceptance. METHODS: The study design uses a mixed methods approach in PNG's coastal and highlands regions. The first research activity will use a qualitative methodology with an epistemological foundation based on constructivism. This design elicits and listens to community members' accounts of ways culture is a rich resource that provides meaning to the COVID-19 pandemic; the design also measures adherence to niupela pasin ("new normal" in Tok Pidgin) and vaccination acceptance. The second activity will be a cross-sectional survey to assess the distribution of features of vaccine acceptance, priorities, and practices. The third activity will be in-depth interviews of health care providers actively involved in either COVID-19 clinical management or public health-related pandemic control activities. RESULTS: The project proposal has been reviewed and approved by the Medical Research Advisory Committee of Papua New Guinea. Qualitative data collection started in December 2022, and the survey will begin in May 2023. The findings will be disseminated to the participating communities later this year, followed by publication. CONCLUSIONS: The proposed research on community views and experiences concerning sociocultural and behavioral features of acceptance of the vaccine will provide a better understanding of communication and education needs for vaccine action for COVID-19 control in PNG and other LMICs. The research also considers the influence of health care providers' and policy makers' roles in the awareness and use of the COVID-19 vaccine. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/44664.
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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 | 0,043 | 0,028 |
| Méta-épidémiologie (sens strict) | 0,005 | 0,003 |
| Méta-épidémiologie (sens large) | 0,006 | 0,006 |
| Bibliométrie | 0,004 | 0,004 |
| Études des sciences et des technologies | 0,005 | 0,003 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,004 | 0,003 |
| Intégrité de la recherche | 0,005 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,055 | 0,008 |
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 ».