14 Measuring the factors associated with the occupational wellbeing of nurses who provide palliative and end of life care
Bibliographic record
Abstract
Introduction Despite the global strides made in palliative and hospice care, organisational challenges continue to negatively impact the accessibility to care and adequacy of pain and symptom management for patients and their family members. Nurses play a fundamental role in advocating for and providing high-quality palliative and end of life care, however, there is limited understanding of their occupational wellbeing. The purpose of this presentation is to explore the occupational resources and demands in palliative nursing practice through the development and psychometric evaluation of the Palliative Care Nursing-Job Resources (PCN-JR) Scale and Palliative Care Nursing- Job Demands (PCN-JD) Scale. Methods This study used a three-phase process of instrument development and psychometric evaluation within a province-wide cross-sectional design. Phase 1 involved a thematic analysis of qualitative data, palliative expert consultation, and a content validation index in the development of a 64-item PCN-JR Scale and 72-item PCN-JD Scale. Phase 2 consisted of a pilot survey of 55 nurses and use of item discrimination analysis to estimate internal consistency reliability and reduce the length of each scale. Exploratory factor analysis was used in Phase 3 to further test the modified scales in a province-wide survey of n=377 nurses who provide palliative and end of life care. Result Exploratory factor analysis of the 32 items related to palliative job resources favoured an 8-factor structure, accounting for 62% of the variance, Cronbach’s alpha 0.90. The 36 items related to palliative job demands favoured an 8-factor structure, accounting for 61% of the variance, Cronbach’s alpha 0.93. Discussion The Palliative Care Nursing-Job Resources (PCN-JR) Scale and Palliative Care Nursing- Job Demands (PCN-JD) Scale are valid and reliable, and have broad applicability to better understand the occupational wellbeing of palliative care nurses. Further research is necessary to further evaluate their psychometric properties from a national and international perspective.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".