End-of-life care in a nursing home: a study of family, nurse and healthcare aide perspectives
Bibliographic record
Abstract
AIM: To examine the perspectives of family members, registered nurses and healthcare aides regarding the last 72 hours of Canadian nursing home residents' lives. STUDY DESIGN: Exploratory, descriptive design using semistructured interviews. SAMPLE: Consisted of 14 registered nurses and eight healthcare aides who had provided care within the last 72 hours before a resident's death and four family members who had visited within the same time frame. SETTING: A 220-bed nursing home located within a larger long-term care facility in Canada. METHODS: Thematic analysis was conducted independently and through consensus identified themes and subthemes emerging from the interviews. FINDINGS: Dyspnea was a more common end-of-life (EoL) symptom for nursing home residents in this sample than was pain. Caring behaviours of staff were central to the resident's dying process and involved assessment, coordination of care, physical care, family education and nurture. Family members' ambivalence about the resident's death and fear of the resident dying alone were frequently noted. CONCLUSIONS: Appropriate and timely symptom management and a range of caring behaviours of staff are critical elements in the dying experience of nursing home residents. Additional education and support for personnel involved with caring for this group will enhance end-of-life care.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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 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".