‘Intensive palliative care’: a qualitative study of issues related to nurses’ care of people with amyotrophic lateral sclerosis at end-of-life
Bibliographic record
Abstract
Background: Amyotrophic lateral sclerosis (ALS) is currently an incurable and fatal disease, which often comes with a high symptom burden at the end-of-life stage. Little is known about nurses' experiences in this context. Objective: To explore the experience of nurses caring for people with ALS at end-of-life. Design: A qualitative multiple-case study design. Method: Individual semi-structured interviews were conducted between February and August 2022 with nurses from Quebec, Canada, who had provided care to at least one person living with ALS at the end-of-life in the past 12 months. The content analysis method was used for data analysis and within-case and cross-case analyses were conducted, as well as comparative analyses according to the type of position held by the participants that determined the cases: (1) home care, (2) hospital and (3) palliative care home. Results: Participating in the study were 24 nurses: 9 were from home care, 8 from hospitals and 7 from palliative care homes. Five main themes were identified: (1) identifying the end-of-life period, (2) communication issues, (3) supporting the need for control, (4) accompanying in the fight culture and (5) the extent of the need for care. A sixth theme was also added in order to report the need expressed by nurses to improve their care of patients living with ALS at end-of-life. Conclusions: Although nurses' experiences varied among the different settings, the study identifies the pressing need for better education and, above all, more resources when caring for a person living with ALS at end-of-life. Future research should explore the experiences of other members of the healthcare team and test interventions designed to improve the quality of life and end-of-life of people living with ALS.
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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 | 0.016 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.014 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| 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".