Death, Dying, and Credibility in Long-Term Care: How Healthcare Aides Were the Voiceless Other During the COVID-19 Pandemic
Bibliographic record
Abstract
Confronted by an unprecedented number of deaths in Long-Term Care (LTC) during the COVID-19 pandemic, society had no choice but to engage in a public discourse about the state of death and dying in LTC, and the staff who were caring for residents: healthcare aides. Despite being places where older adults die, death and dying has largely been hidden within LTC homes, serving to complicate and conceal healthcare aides’ experiences at a time when LTC residents were visibly dying. Although being the subject of public discourse, healthcare aides remained voiceless during the pandemic, their experiences of caring for dying residents overlooked by the testimony of experts. Instead of healthcare aides being invited into a conversation to share their unique knowledge of death and dying in LTC, namely through that of touch and practical wisdom, they experienced a lack of epistemic credibility, having been served a testimonial injustice.
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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 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.038 | 0.056 |
| Scholarly communication | 0.022 | 0.018 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.003 | 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".