Barriers and facilitators of COVID-19 vaccination among drug users: a qualitative analysis for future crisis management
Bibliographic record
Abstract
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.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".