Connecting the experiences of persons with disabilities and social workers in Nigerian care institutions regarding COVID-19 vaccine uptake: a qualitative descriptive-interpretive design
Bibliographic record
Abstract
Background: During the COVID-19 pandemic, persons with disabilities (PWDs) have faced additional disadvantages that have exacerbated their physical and mental health challenges. In Nigeria, where cultural, religious, and informational barriers persist, understanding these factors is critical for improving health interventions, including vaccine uptake among PWDs. Methods: This study employed a qualitative descriptive-interpretive design to explore the perceptions of PWDs regarding the COVID-19 pandemic and the vaccine, alongside social workers' views on their roles in facilitating vaccine uptake. We conducted in-depth semi-structured telephone interviews with 20 participants, comprising 16 PWDs and four social workers in Nigerian rehabilitation homes. Data were analyzed using critical thematic analysis to identify key themes influencing attitudes toward the pandemic and vaccine uptake. Results: The study uncovered significant barriers to COVID-19 vaccine uptake among PWDs, primarily driven by mistrust in government initiatives, widespread conspiracy theories, and deeply held cultural and religious beliefs. Additionally, while social workers played crucial roles as community surveillance officers, in-house educators, and community referral agents, their interventions lacked specific strategies aimed at increasing vaccine uptake among PWDs. Their efforts were more focused on addressing the psychological impacts of the pandemic rather than fostering behavioral changes toward vaccine acceptance. Conclusion: To enhance COVID-19 vaccine uptake among PWDs in Nigerian rehabilitation homes, targeted interventions that address the identified barriers are essential. These should include trust-building measures, culturally and religiously sensitive communication strategies, and tailored educational programs by social workers. Moreover, training social workers in specific, evidence-based strategies to increase vaccine uptake is crucial for mitigating the pandemic's impact on this vulnerable population.
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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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".