Tragedy or ordeal? How reshaping discourses can contribute to reimagining approaches to care and the value of life in times of crisis
Bibliographic record
Abstract
Attending to the discourses surrounding a crisis can offer important insights into who and what is valued in society. The COVID-19 pandemic offers just this opportunity as it represents a modern societal crisis. Our former work, which examined media discourses surrounding life in LTC during the COVID-19 pandemic, revealed a discourse that framed life in LTC as tragic. In this study, using a life story approach, we explore how, if at all, the tragedy discourse shifts when older persons in LTC (n = 15) are invited to share their experiences of COVID-19. Our analysis revealed three interrelated categories that together highlight the myriad of factors impacting the value of life in LTC during the COVID-19 pandemic and which act as counter-narratives to the dominant discourse. These counter-narratives are captured in the categories: COVID-19 as a component of a larger life story; Resisting isolation despite restrictions; and Contrasting experiences of life in LTC before, during, and after COVID-19, and highlight how residents strove to retain a sense of belonging, care and connection during this time of crisis. Introducing counter-narratives of residents who lived through COVID-19 in LTC provides an impetus for reconstructing this period of global unrest from that of a tragedy to that of an ordeal. It also reminds us of the valiant efforts people continue to make to ensure the value of their lives are seen and recognized even when dominant discourses are supporting their erasure.
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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 | 0.027 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.029 | 0.096 |
| Scholarly communication | 0.035 | 0.046 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".