Developing Strategies to Improve Advance Care Planning in Long Term Care Homes: Giving Voice to Residents and Their Family Members
Bibliographic record
Abstract
Long term care (LTC) homes, also known as residential care homes, commonly care for residents until death, making palliative care and advance care planning (ACP) important elements of care. However, limited research exists on ACP in LTC. In particular, research giving voice to family members and substitute decision makers is lacking. The objective of this research was to understand experiences, perspectives, and preferences to guide quality improvement of ACP in LTC. This qualitative descriptive study conducted 34 individual semistructured interviews in two LTC homes, located in Canada. The participants were 31 family members and three staff, consisting of a front line care worker, a registered nurse, and a nurse practitioner. All participants perceived ACP conversations as valuable to provide “resident-centred care”; however, none of the participants had a good understanding of ACP, limiting its effectiveness. Strategies generated through the research to improve ACP were as follows: educating families and staff on ACP and end-of-life care options; better preparing staff for ACP conversations; providing staff skills training and guidelines; and LTC staff initiating systematic, proactive conversations using careful timing. These strategies can guide quality improvement of palliative care and development of ACP tools and resources specific to the LTC home sector.
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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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 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".