Clinical competence in palliative nursing in Norway: the importance of good care routines
Bibliographic record
Abstract
AIM: This paper examines how clinical nurse specialists assessed their competences in relief of symptoms, and explores factors affecting good care routines in palliative care. METHODS: A prospective survey among 235 former post-bachelor (response rate 50.6 %) students at two university colleges in Norway. RESULTS: Correlations between the measured concepts showed a medium to high correlation between all five competences. Use of care routines correlated with all the other factors. The ability to identify lack of care showed significant correlation with one concept: time available for nursing. The results from the regression analysis supported a model with good care routines as a dependent variable (F=22.59, df=91, P<0.001). The independent variables in the model explained almost 57% of the variance in using care routines. Competences dealing with mouth problems, nausea, anxiety and the use of the Edmonton symptom assessment system (ESAS) had a positive effect on care routines. On the other hand, the ability to identify lack of care had a significant negative effect on the use of care routines. CONCLUSIONS: The importance of systematic assessment of the palliative patient;s care needs and symptom management are emphasized, and use of the ESAS, and good care routines was affected by post-bachelor competences.
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 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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".