‘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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".