Tragedy and value of life of older persons in long-term care homes during COVID-19: a critical discourse analysis
Bibliographic record
Abstract
Abstract During the COVID-19 pandemic, the media provided daily coverage of this unprecedented crisis in the history of the 21st century. Some topics, such as how the virus affected older adults, were widely covered. The way in which COVID-19 was documented evoked a ‘tragedy’ narrative through consistent reporting about the suffering it was causing and the deleterious consequences it had on specific populations, including residents of long-term care homes (LTC). This article explores how reports on COVID-19 in LTC homes in a national newspaper (The Globe and Mail) fuelled a tragedy discourse that modulated the value of life of older adults living in those environments. We used critical discourse analysis and analysed 74 articles focusing on older persons residing in LTC homes in two Canadian provinces (Quebec and Ontario) during COVID-19. This article offers a brief overview of the notion of tragedy and how the discourse of tragedy is intertwined with humanitarian crises, life and death, and the value of life. Our findings revealed the construction of three types of tragedies that shape our societal values around life and death in LTC: the tragedy of the threat to life, the tragedy of the unfortunate (old, vulnerable and lacking in agency) and, finally, the tragedy of historical neglect and abandonment. Our findings suggest that the nature of reporting on life and death in LTC homes during the COVID-19 pandemic provoked a sense of fear and pity for a passive other. Re-thinking what gets reported in the media, including whose voice is represented/missing and how tragedy narratives are balanced with contesting stories, could elicit more sentiments of solidarity and action rather than reinforce pity, distancing and immobilisation.
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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 | 0.025 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.022 | 0.030 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| 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".