Exploring the Perceived Competence and Self-Efficacy of Ontario’s Home Care Nurses in Palliative Care Palliative Care Delivery
Bibliographic record
Abstract
Background: With the aging population and rise in comorbidities, the demand for palliative care (PC) continues to grow globally. PC adopts a holistic approach to supporting individuals with life-limiting illnesses by focusing on symptom management, maximizing comfort, and prioritizing quality of life. Integrating PC within the community enables patients to receive care in the comfort of their homes, promotes family involvement, and offers cost-effective solutions. Despite its benefits, home care providers report challenges in their ability to provide competent and effective PC which may impact the overall quality of care delivered to patients and their families. Limited research has explored how nurses perceive their own competence and self-efficacy in PC delivery, particularly in home care settings. 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 and additional questions based on the literature. Study information was disseminated by home care and professional nursing organizations in December 2024. Inclusion criteria included 1) RNs or RPNs, 2) currently working as a home care nurse in Ontario, 3) had at least six months of nursing experience, and 4) had provided PC in patients’ homes. Future Applications: This study will contribute to the expanding body of research on palliative home care and may guide the direction of future research. Highlighting nurses’ educational needs underscores the importance of targeted training to enhance confidence, improve quality of care, and support the retention of nurses in the community.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".