End of Life Communication: A Scoping Review of Conversations on Death and Dying between Long-Term Care Home Staff and Family Caregivers
Bibliographic record
Abstract
Current trends in Canada show that people are admitted into long-term care (LTC) facilities later in life, have more complex and co-morbid health and social care needs and thus have shorter lengths of stay before death (Cable-Williams & Wilson, 2017; Garner et al., 2018). As common occurrences, death and dying should be understood as part of the LTC cultural context, and yet there is a lack of discourse around dying in LTC facilities (O’Connor, 2009). Conversations around death and dying are important, as avoiding or delaying them can potentially restrict residents from timely end-of-life care. The belief that LTC facilities are places for living (not dying) contributes to the likelihood that end-of-life care is initiated when only a few hours or days remain until death, with little or no palliative care provided at earlier stages in the trajectory of decline (Cable-Williams & Rodin, 2016). This scoping review provides a synthesis of existing research around goals of conversations of death and dying between LTC staff and families and barriers and facilitators that can help or hinder having these discussions.
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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 | 0.047 | 0.157 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.027 | 0.024 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".