Protection, freedom, stigma: a critical discourse analysis of face masks in the first wave of the COVID-19 pandemic and implications for medical education
Bibliographic record
Abstract
Background: The COVID-19 pandemic has spotlighted the face mask as an intricate object constructed through the uptake of varied and sometimes competing discourses. We investigated how the concept of face mask was discursively deployed during the first phase of the COVID-19 pandemic. By examining the different discourses surrounding the use of face masks in public domain texts, we comment on important educational opportunities for medical education. Method: We applied critical discourse methodology to look for key phrases related to face masks that can be linked to specific socio-economic and educational practices. We created an archive of 171 English and Mandarin texts spanning the period of February to July 2020 to explore how discourses in Canada related to discourses of mask use in China, where the pandemic was first observed. We analyzed how the uptake of discourses related to masks was rationalized during the first phase of the pandemic and identified practices/processes that were made possible. Results: While the face mask was initially constructed as personal protective equipment, it quickly became a discursive object for rights and freedoms, an icon for personal expression of political views and social identities, and a symbol of stigma that reinforced illness, deviance, anonymity, or fear. Conclusion: Discourses related to face masks have been observed in public and institutional responses to the pandemic in the first wave. Finding from this research reinforce the need for medical schools to incorporate a broader socio-political appreciation of the role of masks in healthcare when training for pandemic responses.
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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 | 0.018 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.018 | 0.034 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".