Empowering Residents and Families for Care Decisions at End of Life
Bibliographic record
Abstract
Emphasis on client‐centred care is the philosophy for most health care institutions. Long‐term care nursing homes have adopted this philosophy as well, with added emphasis on quality End‐of‐Life Care. Medical advancements have made End‐of‐Life care more complex and individuals and families are often asked to make crucial care decisions in the midst of crisis for their loved ones that may not be in accordance with the actual wishes of their loved ones. Fairmount Nursing Home in Glenburnie has been a leader in their expertise in client centered care. This setting provided a welcoming environment for two of the Queen’s 4 th year nursing students to complete a practicum in Community Health Promotion. Our goal was to increase quality of care at End‐of‐Life by stimulating conversations around advanced decision‐ making. The focus was on expanding the knowledge of residents and their families and Substitute Decision Makers and thereby prompting thoughts about discussion before acute illness or crisis. A social assessment and literature search revealed that everyone has “a story to tell” about a personal End‐of‐Life experience. Through the development of a toolkit, reminders in the Fairmount monthly newsletter to advise readers of resources, and a presentation of information and resources at“Family Night”, we intended to encourage earlier important and focused conversations between residents, families and staff. We found that the information we presented promoted both discussion and questions regarding End‐of‐ Life. Strategic work must continue in order to assist people of all ages and stages of life to talk about their values and wishes before a health crisis intervenes and the opportunity for thoughtful choice is lost.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".