Self-Perceived Competence of Nurses and Care Aides Providing a Palliative Approach in Home, Hospital, and Residential Care Settings: A Cross-Sectional Survey
Bibliographic record
Abstract
Background and purpose A palliative approach involves adapting and integrating palliative care knowledge and expertise earlier on and across sectors of care for people who have life-limiting chronic conditions. This study explored the extent to which nurses’ and care aides’ self-perceived palliative care competence may explain variation in the application of a palliative approach across nursing care settings that do not specialize in palliative care. A secondary objective was to psychometrically evaluate an instrument for measuring self-perceived palliative care competence. Methods and procedures: Data were collected via a cross-sectional survey (N = 1468) of registered nurses, licensed practical nurses, and care aides at 114 randomly selected hospital-based medical units, home care offices, and residential care facilities. The questionnaire included the Palliative Care Nursing Self-Competence Scale. Multilevel logistic regression and multigroup confirmatory factor analyses were conducted. Results In addition to self-perceived competence, factors associated with a palliative approach include identification of patients who have life-limiting conditions and who would benefit from a palliative approach, and work environment. The psychometric analyses of the Palliative Care Nursing Self-Competence Scale confirmed a 10-dimensional structure, strong internal consistency reliability, and measurement equivalence. Discussion and conclusion: This study provides information for future development and research on interventions for integrating a palliative approach.
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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 | 0.003 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".