ArticleHealth science reports2025
Vaccine Hesitancy in Public Healthcare During Pandemics: An International Study to Inform Management Learning.
Article in Health science reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Why do people trust (or mistrust) health organizations? A systematic review.International journal for equity in health · 2026Pooled it
- HPV vaccine uptake in Pakistan: A qualitative study to identify barriers and facilitators before the first HPV national rollout.Human vaccines & immunotherapeutics · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: This paper explores vaccine hesitancy through the lens of management learning in public healthcare during pandemics. It addresses the need for qualitative insights from active academics, focusing on their uncertainties and ambivalence regarding COVID-19 vaccination. The study aims to deepen understanding of vaccine hesitancy during the pandemic from a management learning perspective, examining healthcare systems, governance, and community trust. Methods: Using a qualitative approach, the research draws from a multidisciplinary research network in health and digital society. A total of 27 scholars from 17 countries participated in an open-ended questionnaire designed to elicit insights on the strategies, ethics, and public responses associated with national COVID-19 vaccination efforts. Data collection occurred from May 2021 to July 2021, during the initial rollout of vaccines to broader populations. The analysis employed a hermeneutical framework, using thematic analysis to interpret textual data. Illustrative accounts enriched the contextual understanding. Results: The resulting themes are information and disinformation; social inclusion and exclusion; trust and distrust; individual liberties and collective constraints. The findings indicate that individual nations actions play a role in shaping public discourse, opinion, and political responses related to vaccination, despite a globalized context. The analysis reveals that historical and political factors significantly influence public health policies and perceptions of vaccine hesitancy, together with the importance of information and dialogue with various stakeholders to create trust. Conclusions: The COVID-19 crisis, characterized by threats and uncertainties, has strained trust in public health institutions. A management learning perspective can be adopted that embraces a comprehensive understanding of the complexities surrounding COVID-19 vaccination. By fostering collaborative learning and stakeholder engagement, public health organizations can enhance their responsiveness and build trust within communities.
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Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.