Evidence map›Paper›PMID 36440070›Full record

ArticleCanadian medical education journal2022

Evolving discourses of COVID-19 and implications for medical education: a critical discourse analysis.

Vincent Tang, Asia van Buuren, Maria Athina Tina Martimianakis

Abstract read
In one paragraph

Article in Canadian medical education journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. The foundations of influencing policy and practice: How risk science discourse shaped government action during COVID-19.Risk analysis : an official publication of the Society for Risk Analysis · 2024
    Article
  2. Article
  3. 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

3 authors.

Vincent TangDepartment of Psychiatry, University of Toronto, Ontario, Canada.
Asia van BuurenDepartment of Pediatrics, University of British Columbia, British Columbia, Canada.
Maria Athina Tina MartimianakisDepartment of Pediatrics, University of Toronto, Ontario, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The othering of individuals has been identified as a concern during the COVID-19 pandemic. The purpose of this study was to examine public commentary during early stages of the pandemic for: 1) emerging discourses that highlighted population-level inequities, and 2) the implications these discourses may have for medical education. Methods: Using a critical discourse analysis (CDA) approach, an archive of texts available in the public domain discussing COVID-19 was iteratively created, reviewed, and coded. We used an intersectional framework to analyze how COVID-19 highlighted structural and institutional inequity at the population level. Results: We found 86 representative texts published from March to June 2020. We focused our analysis on implications within Ontario. The two major discourses that emerged were "COVID-19 as Equalizer" and "COVID-19 as Discriminator." The former emerged in the early stages of the pandemic to mobilize public health recommendations and describe near-universal impacts on the public. The latter followed to highlight new and pre-existing forms of marginalization exacerbated by the pandemic. Conclusions: This study provides a unique perspective on how structural and systemic responses to COVID-19 were shaped through analysis of public discourse, and therefore, has implications for how the COVID-19 pandemic and future pandemics are framed for future medical learners.

Identifiers

PMID36440070
PMCPMC9684037

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.