A Foucauldian discourse analysis of media reporting on the nurse‐as‐hero during COVID‐19
Bibliographic record
Abstract
This study uses a Foucauldian discourse analysis to explore media reporting on the role of nurses as being consistently positioned 'heroes' during COVID-19. In so doing, it highlights multiple intersecting discourses at play, with the caring discourse acting as a central one in negatively impacting nurses' ability to advocate for safe working conditions during a public health emergency. Drawing on media reports during the outbreak of COVID-19 in Ontario, Canada in the spring of 2020 and on historical information from SARS, this study seeks to establish caring as a discourse and examine if the caring discourse impedes nurses' ability to protect themselves from harm. The results of this analysis explicate how public media discourses that position nurses as caring, sacrificial and heroic may have impacted their ability to maintain their personal safety as a result of the expectations put upon the nursing profession.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".