Supporting community engagement around end of life conversations & care, an asset- based approach
Bibliographic record
Abstract
Healthcare systems and local hospices have extensive expertise and community connections around end of life awareness and care, however innovative and cost effective solutions are required to engage the wider (healthy, non-cancer, non-hospice) community if we are to align public preferences with outcomes of care at the local/community level in the future. ▶ Most adults have personal experience around death, dying, and bereavement. ▶ Leaders of community organisations are no exception. ▶ However, the varied backgrounds (personal and professional) must be given a common base upon which to lay the foundation for community-led engagement around this complex and taboo subject. An innovative programme is proving to be effective inspiring and engaging communities in the Northwest (UK) to launch awareness initiatives, overcoming the taboo of talking about death and dying, improving awareness of the need for advance care conversations, and increasing access to available community-based information and resources. Using a facilitated asset-based approach, partnership projects have been launched with funders, healthcare, hospices, and community agencies across seven communities (rural/city). Outcomes and evaluations to date from more than 150 participating organisations will be shared.
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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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.030 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".