‘Confidence and fulfillment’: a qualitative descriptive study exploring the impact of palliative care training for long-term care physicians and nurses
Bibliographic record
Abstract
Objective: To explore the impact of a 2-day, in-person interprofessional palliative care course for staff working in long-term care (LTC) homes. Methods: A qualitative descriptive study design was employed. LTC staff who had participated in Pallium Canada's Learning Essential Approaches to Palliative Care LTC Course in Ontario, Canada between 2017 and 2019 were approached. Semi-structured interviews were conducted, using an online videoconferencing platform in mid-2021 in Ontario, Canada. These were done online, recorded, and transcribed. Data were coded inductively. Results: Ten persons were interviewed: four registered practical nurses, three registered nurses, one nurse practitioner, and two physicians. Some held leadership roles. Participants described ongoing impact on themselves and their ability to provide end-of-life (EOL) care (micro-level), their services and institutions (meso-level), and their healthcare systems (macro-level). At a micro-level, participants described increased knowledge and confidence to support residents and families, and increased work fulfillment. At the meso-level, their teams gained increased collective knowledge and greater interprofessional collaboration to provide palliative care. At the macro level, some participants connected with other LTC homes and external stakeholders to improve palliative care across the sector. Training provided much-needed preparedness to respond to the impact of the COVID-19 pandemic, including undertaking advance care planning and EOL conversations. The pandemic caused staff burnout and shortages, creating challenges to applying course learnings. Significance of results: The impact of palliative care training had ripple effects several years after completing the training, and equipped staff with key skills to provide care during the COVID-19 pandemic. Palliative care education of staff remains a critical element of an overall strategy to improve the integration of palliative care in 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 | 0.020 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".