Evolving discourses of COVID-19 and implications for medical education: a critical discourse analysis
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.037 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.023 | 0.040 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".