Delivering person-centred palliative care in long-term care settings: is humanism a quality of health-care employees or their organisations?
Bibliographic record
Abstract
Abstract Reflecting on sustained calls for patient-centredness and culture change in long-term care, we evaluated the relative importance of personal and organisational predictors of palliative care, hypothesising the former as weaker predictors than the latter. Health-care employees (N = 184) from four Canadian long-term care homes completed a survey of person-centred care, self-efficacy, employee wellbeing and occupational characteristics. Using backward stepwise regression models, we examined the relative contributions of these variables to person-centred palliative care. Specifically, blocks of variables representing personal, organisational and occupational characteristics; palliative care self-efficacy; and employee wellbeing were simultaneously regressed on variables representing aspects of person-centred care. The change in R2 associated with the removal of each block was examined to determine each block's overall contribution to the model. We found that occupational characteristics (involvement in care planning), employee wellbeing (compassion satisfaction) and self-efficacy were reliably associated with person-centred palliative care (p < 0.05). Facility size was not associated, and facility profit status was less consistently associated. Demographic characteristics (gender, work experience, education level) and some aspects of employee wellbeing (burnout, secondary trauma) were also not reliably associated. Overall, these results raise the possibility that humanistic care is less related to intrinsic characteristics of employees, and more related to workplace factors, or to personal qualities that can be cultivated in the workplace, including meaningful role engagement, compassion and self-efficacy.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".