Barriers and facilitators of COVID-19 vaccination among drug users: a qualitative analysis for future crisis management
Notice bibliographique
Résumé
INTRODUCTION: People Who Use Drugs (PWUD), are a population at the high risk of exposure to infectious disease and should be considered as a priority for vaccination against communicable diseases. However, during the COVID-19 pandemic this population showed resistance to vaccination. The aim of this study was to better understand the barriers and facilitators of COVID-19 vaccination in PWUD in Tehran, Iran. MATERIALS AND METHODS: In this qualitative study data were collected through semi-structured interviews with participants through purposeful sampling with maximum variation. The collected data were analyzed using content analysis informed by Graneheim and Lundman using MAXQDA-10 Software. Lincoln and Guba's criteria were used to ensure the accuracy and validity of the data. FINDINGS: Our study results were presented under two main themes: barriers and facilitators to COVID-19 vaccination acceptance among PWUD. Based on the results of this study, the most important barriers to COVID-19 vaccine acceptance were; Stereotyped beliefs (The belief that drug users will not get infected with COVID-19, Ineffectiveness of COVID-19 vaccine, The negative effect of vaccine on underlying disease, Lack of trust in healthcare system and the type of vaccine), Low health literacy and knowledge (Neglecting health and underestimating the disease, Not prioritizing the health, Low health literacy, Believing in self-treatment and traditional medicine, Available rumors), low social capital(including having limited social networks, believing misinformation and perceived powerlessness), Structural and Experiential Barriers (Lack of access to vaccine, Unpleasant past experiences in related with the vaccination), and fear and worry caused by previous experiences(Death or illness of friends/people around who had been vaccinated, Fear of the vaccine). In addition, the most important facilitators of COVID-19 vaccine acceptance can also be classified into 2 categories of The role of incentives and social responsibility(Incentive payments, Social responsibility, Immune system strengthening as a motivation for vaccination) and Rebuilding Trust and Improving Public Perceptions (Compensating for past mistakes, The effect of advertisement by physicians and officials). CONCLUSION: Given the possibility of future pandemics the role of vaccination in the prevention and control of communicable diseases, it is imperative to reduce the most negative consequences of pandemics for the general public and high-risk groups. Barriers to vaccination can be minimized through effective engagement with different social groups the goal of which is to effectively explain the benefits of vaccination. When planned and implemented well this will maintain health and prevent deaths in future pandemics. Health care policymakers can use the results of this study to reduce the barriers to vaccination and encourage high risk social groups to receive vaccination in future pandemics.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,007 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,004 | 0,003 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».