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Record W4378715583 · doi:10.2196/42958

COVID-19 Vaccine Hesitancy Among Patients Recovered From COVID-19 Infection in Wuhan, China: Cross-Sectional Questionnaire Study

2023· article· en· W4378715583 on OpenAlexvenueno aff
Yiman Huang, Ling Zhang, Jiaqi Fu, Yijin Wu, Hao Wang, Weijun Xiao, You Xin, Zhenwei Dai, Mingyu Si, Xu Chen, Mengmeng Jia, Zhiwei Leng, Dan Cui, Xiaoyou Su

Bibliographic record

VenueJMIR Public Health and Surveillance · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsnot available
FundersNational Health Commission of the People's Republic of ChinaChinese Academy of Medical Sciences
KeywordsVaccinationMedicineCoronavirus disease 2019 (COVID-19)Cross-sectional studyOdds ratioAsymptomaticLogistic regressionHerd immunitySevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Confidence intervalImmunologyInternal medicineDiseaseInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

BACKGROUND: Although patients recovered from COVID-19 already have immunity gained from natural infection, they are still at risk of reinfection due to the emergence of new variants of COVID-19 and the diminishing of naturally acquired immunity over time. Vaccination is associated with efficacious protection against COVID-19 infection and could boost infection-acquired immunity; however, various COVID-19 survivors have not been vaccinated due to vaccine hesitancy. OBJECTIVE: The aim of this study was to investigate COVID-19 vaccine hesitancy and related factors among COVID-19 survivors. METHODS: A cross-sectional questionnaire survey was conducted among patients who recovered from COVID-19 infection in Wuhan, China, between June 10 and July 25, 2021. The questionnaire included sociodemographic information, items on COVID-19 infection, the COVID-19 vaccine hesitancy scale based on the 3Cs (complacency, convenience, and confidence) model, trust in vaccine manufacturers and health facilities, and reasons for the decision to accept COVID-19 vaccination. Multivariate logistic regression analysis was used to assess the factors influencing COVID-19 vaccine hesitancy. RESULTS: Among the 1422 participants, 538 (37.8%) were not vaccinated against COVID-19. The COVID-19-recovered patients who self-reported having a current unhealthy status expressed more hesitancy about the COVID-19 vaccine than those who perceived themselves to be healthy (odds ratio [OR] 0.45, 95% CI 0.28-0.71). Compared to the asymptomatic patients, patients with mild symptoms were more likely to receive a COVID-19 vaccine (OR 1.67, 95% CI 1.02-2.82). Regarding the 3Cs model, high complacency (P=.005) and low convenience (P=.004) were significant negative factors for COVID-19 vaccination. Trust in vaccine manufacturers and health facilities was a significant positive factor for COVID-19 vaccination (OR 1.14, 95% CI 1.09-1.19). "Self-needs" was the main reason for patients to receive the COVID-19 vaccine, whereas "already have antibodies and do not need vaccination" was the main reason for patients to not receive the COVID-19 vaccine. CONCLUSIONS: Among the three major factors of vaccine hesitancy, complacency proved to be the most notable among COVID-19-recovered patients. Therefore, educational campaigns can focus on raising the awareness of risk of infection and the benefits of vaccination to reduce complacency toward vaccination among this population. In particular, for individuals who have recovered from COVID-19, improving factors related to convenience such as transportation, the environment of vaccination, and providing door-to-door service was also deemed necessary to facilitate their vaccination. In addition, addressing the concerns about vaccination of COVID-19-recovered patients could foster trust and promote their uptake of vaccination.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.195
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.043
GPT teacher head0.373
Teacher spread0.330 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2023
Admission routes1
Has abstractyes

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