Evidence map›Paper›PMID 36051512›Full record

ArticleJournal of public health in Africa2022

Perceived accessibility, and adequacy of COVID-19 related information in Nigeria.

Chinenye Ifeoma Ubah, Linda Odikpo, Lovelyn Ndubuisi-Okoroezi, Chisom Mbadugha, Jennifer Ikechukwu-Okoroezi

Abstract read
In one paragraph

Article in Journal of public health in Africa, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. Article
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

5 authors.

Chinenye Ifeoma UbahMid and South Essex NHS Foundation Trust, Essex, UK.
Linda OdikpoDepartment of Nursing Sciences, Nnamdi Azikiwe University, Awka, Nigeria.
Lovelyn Ndubuisi-OkoroeziEast Kent Hospitals University NHS Foundation Trust, UK.
Chisom MbadughaDepartment of Nursing Sciences, University of Nigeria, Nsukka, Nigeria.
Jennifer Ikechukwu-OkoroeziDepartment of Nursing Sciences, Abia State University, Uturu, Nigeria.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Information on COVID-19 has evolved and blended with fake news, which the public, unfortunately, has to make an individual decision on how to use. As a result, access to authentic and adequate health information on COVID-19 is crucial for curbing the ongoing pandemic. The study was aimed at identifying sources of information on COVID-19 commonly used by adult Nigerian residents; determine the adequacy of information received; determine the accessibility of information on COVID-19 among Nigerians, and explore the relationship between location and access to information. An adapted version of the World Health Organization's (WHO) COVID-19 behavioral insight questionnaire was used to collect data from 1,039 adult residents in Nigeria across the geopolitical zones through an online survey. Analysis was done using SPSS version 24. Logistic regression was used to examine if location predicts access to information. Social media was identified as the major source of information among Nigerians. The top three accessible sources included social media 807(77.7%), television 546 (52.6%), and WHO websites 340 (32.7%). It was also found that they perceived information received on COVID-19 as adequate. The logistic regression model of the location did not predict access to COVID-19 information (p<0.05; 95% CI). Health authorities like the WHO, the ministry of health, CDC should optimize social media for better health information coverage.

Indexed as

accessibilityadequacyCOVID-19informationNigeria

Identifiers

PMID36051512
PMCPMC9425939

What OpenQuestion holds

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LicenceCC BY-NC
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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.