“Where it’s okay if we die”: Exploring Older Canadians’ Perspective on Long-Term Care Through Found Poetry
Bibliographic record
Abstract
The thought of living in a nursing home may be disheartening as long-term care establishments have been poorly perceived for decades. The government oversight for quality of care in long-term care homes (LTCH) has resulted in persistent shortcomings when it comes to residents' well-being and health. The coronavirus disease 2019 (COVID-19) pandemic both exacerbated and unveiled long-standing issues regarding the treatment of older adults. Public perceptions about quality of care provided in LTCH declined during the pandemic. With magnification focused on organizational issues in LTCH, future care receivers expressed firm reluctance to consider residence in such facilities. Understanding of older adults' perspectives on LTCH is essential for tailoring care practices and policies. In this study we conducted 2 rounds of interviews with community-dwelling older adults aged 60 or over to better understand their perceptions of LTCH. The narrative data were analyzed using found poetry as an artistic inquiry. Six poems were composed, combining participants' words into one poetic voice-addressing themes such as death, isolation, ongoing health care challenges and private care. Found poetry allowed for salient words to emerge, creating space for nuanced expression of emotions. The combination of multiple voices added to the depth of the poems, which were grounded in the participants' reality.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.034 | 0.019 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".