Evidence map›Paper›PMID 35245755›Full record

ArticleSocial science & medicine (1982)2022

Applying an extended protection motivation theory to predict Covid-19 vaccination intentions and uptake in 50-64 year olds in the UK.

Bethany Griffin, Mark Conner, Paul Norman

Open access · hybridAbstract read
In one paragraph

Article in Social science & medicine (1982), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed
6.5field-weighted citation impact, top 3% 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

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3 · Its place in the literature

Who cites it

24 citing papers in PubMed, 41 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.

Bethany GriffinUniversity of Sheffield, UK.
Mark ConnerUniversity of Leeds, UK.
Paul NormanUniversity of Sheffield, UK. Electronic address: p.norman@sheffield.ac.uk.
University of Sheffield · GBUniversity of Leeds · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo examine the correlates of Covid-19 vaccination intentions and subsequent uptake as outlined in an extended version of protection motivation theory (PMT).

designA two-wave online survey conducted at the start of the vaccination rollout to 50-64 year olds in the UK and three months later. MEASURES: Unvaccinated UK adults (N = 438) aged 50-64 completed baseline measures from PMT (perceived vulnerability, perceived severity, maladaptive response rewards, response efficacy, self-efficacy, response costs, intention) as well as measures of injunctive and descriptive norms, demographics, Covid-19 experiences, and past influenza vaccine uptake. Self-reported uptake of a Covid-19 vaccination was assessed three months later (n = 420).

resultsThe extended PMT explained 59% of the variance in Covid-19 vaccination intentions, after controlling for demographics, Covid-19 experiences, and past influenza vaccine uptake. All extended PMT variables, with the exception of perceived severity and descriptive norms, were significant independent predictors of intention. In line with national figures, 94% of the sample reported having received a Covid-19 vaccination at follow-up with intention found to be the key predictor of uptake.

conclusionsInterventions to increase Covid-19 vaccination uptake need to increase intentions to be vaccinated by emphasizing the benefits of vaccination (e.g., in terms of reducing risk) and likely approval from others while also addressing the concerns (e.g., safety issues) and common misperceptions (e.g., natural immunity versus vaccines) that people might have about Covid-19 vaccines. Future research is needed in countries, and on groups, with lower uptake rates.

Indexed as

COVID-19Influenza VaccinesAdultCOVID-19 VaccinesHumansIntentionMiddle AgedMotivationUnited KingdomVaccinationCOVID-19 VaccinesInfluenza VaccinesCoronavirusCOVID-19HesitancyIntentionNormsProtection motivation theoryVaccinationVaccine

Identifiers

PMID35245755
PMCPMC8867961
OpenAlexW4213421659

What OpenQuestion holds

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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.