‘Bare-bones’ to ‘silver linings’: lessons on integrating a palliative approach to care in long-term care in Western Canada
Bibliographic record
Abstract
BACKGROUND: 'Whole-person' palliative approaches to care (PAC) are important for enhancing the quality of life of residents with life-limiting conditions in long-term care (LTC). This research is part of a larger, four province study, the 'SALTY (Seniors Adding Life to Years)' project to address quality of care in later life. A Quality Improvement (QI) project to integrate a PAC (PAC-QI) in LTC was implemented in Western Canada in four diverse facilities that varied in terms of ownership, leadership models, bed size and geography. Two palliative 'link nurses' were hired for 1 day a week at each site over a two-year time frame to facilitate a PAC and support education and training. This paper evaluates the challenges with embedding the PAC-QI into LTC, from the perspectives of the direct care, or front-line team members. Sixteen focus groups were undertaken with 80 front-line workers who were predominantly RNs/LPNs (n = 25), or Health Care Aides (HCAs; n = 32). A total of 23 other individuals from the ranks of dieticians, social workers, recreation and rehabilitation therapists and activity coordinators also participated. Each focus group was taped and transcribed and thematically analyzed by research team members to develop and consolidate the findings related to challenges with embedding the PAC. RESULTS: Thematic analyses revealed that front-line workers are deeply committed to providing high quality PAC, but face challenges related to longstanding conditions in LTC notably, staff shortages, and perceived lack of time for providing compassionate care. The environment is also characterized by diverse views on what a PAC is, and when it should be applied. Our research suggests that integrated, holistic and sustainable PAC depends upon access to adequate resources for education, training for front-line care workers, and supportive leadership. CONCLUSIONS: The urgent need for integrated PAC models in LTC has been accentuated by the current COVID-19 pandemic. Consequently, it is more imperative than ever before to move forwards with such models in order to promote quality of care and quality of life for residents and families, and to support job satisfaction for essential care workers.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.033 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".