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Record W4318027337 · doi:10.2196/39994

Understanding Drivers of Vaccine Hesitancy During the COVID-19 Pandemic Among Older Adults in Jiangsu Province, China: Cross-sectional Survey

2023· article· en· W4318027337 on OpenAlexvenueno aff
Liuqing Yang, Lili Ji, Qiang Wang, Guoping Yang, Shixin Xiu, Tingting Cui, Naiyang Shi, Lin Zhu, Xuepeng Xu, Hui Jin, Shiqi Zhen

Bibliographic record

VenueJMIR Formative Research · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsnot available
FundersJiangnan UniversityGovernment of Jiangsu Province
KeywordsConfidence intervalSocioeconomic statusVaccinationDemographyMedicineCross-sectional studyPandemicStructural equation modelingCoronavirus disease 2019 (COVID-19)Multilevel modelEnvironmental healthGerontologyPopulationImmunologyInternal medicineInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

BACKGROUND: Older adults are particularly at risk from infectious diseases, including serve complications, hospitalization, and death. OBJECTIVE: This study aimed to explore the drivers of vaccine hesitancy among older adults based on the "3Cs" (confidence, complacency, and convenience) framework, where socioeconomic status and vaccination history played the role of moderators. METHODS: A cross-sectional questionnaire survey was conducted in Jiangsu Province, China, between June 1 and July 20, 2021. Older adults (aged ≥60 years) were recruited using a stratified sampling method. Vaccine hesitancy was influenced by the 3Cs in the model. Socioeconomic status and vaccination history processed through the item parceling method were used to moderate associations between the 3Cs and hesitancy. Hierarchical regression analyses and structural equation modeling were used to test the validity of the new framework. We performed 5000 trials of bootstrapping to calculate the 95% CI of the pathway's coefficients. RESULTS: A total of 1341 older adults participated. The mean age was 71.3 (SD 5.4) years, and 44.7% (599/1341) of participants were men. Confidence (b=0.967; 95% CI 0.759-1.201; P=.002), convenience (b=0.458; 95% CI 0.333-0.590; P=.002), and less complacency (b=0.301; 95% CI 0.187-0.408; P=.002) were positively associated with less vaccine hesitancy. Socioeconomic status weakened the positive effect of low complacency (b=-0.065; P=.03) on low vaccine hesitancy. COVID-19 vaccination history negatively moderated the positive association between confidence (b=-0.071; P=.02) and lower vaccine hesitancy. CONCLUSIONS: Our study identified that confidence was the more influential dimension in reducing vaccine hesitancy among older adults. COVID-19 vaccination history, as well as confidence, had a positive association with less vaccine hesitancy and could weaken the role of confidence in vaccine hesitancy. Socioeconomic status had a substitution relationship with less complacency, which suggested a competitive positive association between them on less vaccine hesitancy.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score0.131

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.148
GPT teacher head0.432
Teacher spread0.284 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations5
Published2023
Admission routes1
Has abstractyes

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