Evidence map›Paper›PMID 38541350›Full record

ArticleInternational journal of environmental research and public health2024

Content Analysis of Official Public Health Communications in Ontario, Canada during the COVID-19 Pandemic.

Maya Fields, Kelsey L Spence

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

2 authors.

Maya FieldsDepartment of Population Medicine, Ontario Veterinary College, University of Guelph, Guelph, ON N1G 2W1, Canada.ORCID 0009-0000-2280-1376
Kelsey L SpenceDepartment of Population Medicine, Ontario Veterinary College, University of Guelph, Guelph, ON N1G 2W1, Canada.ORCID 0000-0002-3948-8598

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Effective communication by governmental organizations is essential to keep the public informed during a public health emergency. Examining the content of these communications can provide insight into their alignment with best practices for risk communication. We used content analysis to determine whether news releases by the Ontario government contained key elements of effective risk communication, as outlined by the Health Canada and Public Health Agency of Canada Strategic Risk Communication Framework. News releases between 25 January 2020 and 31 December 2022 were coded following the five elements of the framework: situational transparency, stakeholder-centered content; evidence-based rationales for decisions; continuous improvements in updating information; and descriptions of risk management. All 322 news releases contained at least one element of the framework, and all five elements were identified at least once across the dataset. Risk management, transparency, and stakeholder-centered content were the most frequently identified elements. News releases near the beginning of the pandemic contained most elements of the framework; however, over time, there was an increase in the use of vague language and lack of evidence-based rationales. Increasing transparency regarding evidence-based decisions, as well as changes in decisions, is recommended to improve risk communication and increase compliance with public health measures.

Indexed as

COVID-19Health CommunicationCanadaCommunicationHumansOntarioPandemicsPublic Healthcontent analysisCOVID-19public healthrisk communication

Identifiers

PMID38541350
PMCPMC10970262

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.