Barriers and facilitators to optimal supportive end-of-life palliative care in long-term care facilities: a qualitative descriptive study of community-based and specialist palliative care physicians’ experiences, perceptions and perspectives
Bibliographic record
Abstract
OBJECTIVE: The COVID-19 pandemic has highlighted ongoing challenges to optimal supportive end-of-life care for adults living in long-term care (LTC) facilities. A supportive end-of-life care approach emphasises family involvement, optimal symptom control, multidisciplinary team collaboration and death and bereavement support services for residents and families. Community-based and palliative care specialist physicians who visit residents in LTC facilities play an important role in supportive end-of-life care. Yet, perspectives, experiences and perceptions of these physicians remain unknown. The objective of this study was to explore barriers and facilitators to optimal supportive end-of-life palliative care in LTC through the experiences and perceptions of community-based and palliative specialist physicians who visit LTC facilities. DESIGN: Qualitative study using semi-structured interviews, basic qualitative description and directed content analysis using the COM-B (capability, opportunity, motivation - behaviour) theoretical framework. SETTING: Residential long-term care. PARTICIPANTS: 23 physicians who visit LTC facilities from across Alberta, Canada, including both in urban and rural settings of whom 18 were community-based physicians and 5 were specialist palliative care physicians. RESULTS: Motivation barriers include families' lack of frailty knowledge, unrealistic expectations and emotional reactions to grief and uncertainty. Capability barriers include lack of symptom assessment tools, as well as palliative care knowledge, training and mentorship. Physical and social design barriers include lack of dedicated spaces for death and bereavement, inadequate staff, and mental health and spiritual services of insufficient scope for the population. CONCLUSION: Findings reveal that validating families' concerns, having appropriate symptom assessment tools, providing mentorship in palliative care and adapting the physical and social environment to support dying and grieving with dignity facilitates supportive, end-of-life care within LTC.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".