Evidence map›Paper›PMID 34202016›Full record

ArticleVaccines2021

Predictors of Intention to Vaccinate against COVID-19 in the General Public in Hong Kong: Findings from a Population-Based, Cross-Sectional Survey.

Elsie Yan, Daniel W L Lai, Vincent W P Lee

Open access · goldAbstract read
In one paragraph

Article in Vaccines, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed, 1 pooled it
5.1field-weighted citation impact, top 4% of its field
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

30 citing papers in PubMed, 1 synthesis or guideline pooled it, 44 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 2 institutions in 1 country.

Elsie YanDepartment of Applied Social Sciences, The Hong Kong Polytechnic University, Hong Kong 999077, China.
Daniel W L LaiFaculty of Social Sciences, Baptist University Hong Kong, Hong Kong 999077, China.
Vincent W P LeeDepartment of Applied Social Sciences, The Hong Kong Polytechnic University, Hong Kong 999077, China.ORCID 0000-0003-4574-636X
Hong Kong Polytechnic University · HKHong Kong Baptist University · HK

Funding

Health and Medical Research Fund COVID190216
6 · The paper itself

Abstract

Vaccination is one of the most effective ways to stop the spread of COVID-19. Understanding factors associated with intention to receive COVID-19 vaccines is the key to a successful vaccination programme. This cross-sectional study explored the rate of vaccination intention and identified its predictors using the health belief model (HBM) in the general population in Hong Kong during the pandemic. Data were collected between December 2020 and January 2021 via telephone surveys. Hierarchical logistic regression analysis was used to identify factors associated with intention to receive COVID-19 vaccines. A total of 1255 adults (>18 years, 53% female) completed the telephone survey. Overall, 42% indicated an intention to vaccinate, 31.5% showed vaccine hesitancy, and 26.5% reported refusal to receive any COVID-19 vaccines. Individuals who were men, older in age, working, with past experiences of other pandemics, less concerned with the vaccine safety, with poorer knowledge about COVID-19, and having greater levels of perceived susceptibility, self-efficacy, cues to action, and acceptance of governmental preventive measures related to COVID-19 were significantly more likely to report an intention to vaccinate. The low intention among the Hong Kong population reflects the importance of developing effective vaccination promotion campaigns with the predictors identified in this study.

Indexed as

COVID-19health belief modelHong Kongintention to vaccinate

Identifiers

PMID34202016
PMCPMC8310118
OpenAlexW3176987035

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.