Home Care Nurses’ Perceived Competence and Self-Efficacy Regarding Palliative Care: A Cross-Sectional Study
Bibliographic record
Abstract
Background: The demand for palliative care (PC) continues to grow globally due to the increase in the aging population. Integrating community-based PC enables patients to receive care in their homes, promotes family involvement, and is cost-effective. Despite its benefits, nurses report challenges in delivering competent PC, which impacts the quality of care delivered to patients and their families. Objective: This cross-sectional study explored Ontario home care nurses’ perceived level of competence and self-efficacy in PC delivery. Methods: An online survey was created using two validated scales. The surveys were disseminated through home care and professional nursing organizations. Inclusion criteria were 1) Registered Nurses or Registered Practical Nurses, 2) currently working as a home care nurse in Ontario, 3) having at least six months of nursing experience, and 4) having provided PC in patients’ homes. Results: Findings from the survey suggest a positive association (ρ = .69, p < .001) between perceived competence and self-efficacy. Nurses reported the lowest perceived competence was in ‘spiritual care’ and the lowest self-efficacy was in ‘symptom management.' Organizational and workplace environments showed a significant association with nurses’ perceived competence and self-efficacy, further highlighting the importance of hands-on, real-world application. Conclusion: This study underscores the importance of PC education, targeted training, and supportive workplace environments in enhancing nurses’ confidence in PC delivery. These factors support the enhancement of PC delivery, the retention of community PC nurses, and the sustainability of palliative home 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".