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Supporting community engagement around end of life conversations & care, an asset- based approach

2012· article· en· W2112147527 on OpenAlexaff
M Matthiesen, J R Ashton, Elaine Owen, Katherine Froggatt, Jacob McKnight, J. Kretzmann

Bibliographic record

VenueBMJ Supportive & Palliative Care · 2012
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsWestern University
Fundersnot available
KeywordsTabooPublic relationsGeneral partnershipCommunity engagementEnd-of-life careAsset (computer security)Local communityHealth careNursingBusinessSociologyPolitical sciencePalliative careMedicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.421
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.323
GPT teacher head0.482
Teacher spread0.159 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2012
Admission routes1
Has abstractyes

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