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Record W4389571501 · doi:10.1017/s0714980823000624

Age-Friendly Communities: Are they also “Friendly” for Death, Dying, Grief, and Bereavement?

2023· article· en· W4389571501 on OpenAlexafffund
Julia Brassolotto, Albert Banerjee

Bibliographic record

VenueCanadian Journal on Aging / La Revue canadienne du vieillissement · 2023
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsSt. Thomas UniversityUniversity of Lethbridge
FundersSocial Sciences and Humanities Research Council of CanadaAlberta InnovatesFondation de la recherche en santé du Nouveau-Brunswick
KeywordsGriefPalliative careInclusion (mineral)Value (mathematics)PsychologyGerontologyPublic relationsNursingMedicinePolitical sciencePsychotherapistSocial psychology

Abstract

fetched live from OpenAlex

The age-friendly movement aims to ensure that people can live healthy and meaningful lives as they age. It is committed to activity and inclusion, with policies, services, and structures that enable older adults to remain engaged in activities that they value. We suggest that there is further opportunity for communities to increase inclusion and reduce ageism by improving their "death-friendliness". A death-friendly approach could lay the groundwork for a community in which people do not fear getting old or alienate those who have. To this end, we consider the merits of the compassionate communities framework which has emerged out of palliative care and critical public health. Compassionate communities focus on end-of-life planning, bereavement support, and improved understandings about aging, dying, death, loss, and care. The age-friendly and compassionate communities initiatives are complementary in their objectives but have not yet converged in practice. We suggest that they should.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0110.020
Scholarly communication0.0100.015
Open science0.0020.011
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.041
GPT teacher head0.306
Teacher spread0.265 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations3
Published2023
Admission routes2
Has abstractyes

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Same venueCanadian Journal on Aging / La Revue canadienne du vieillissementSame topicAging and Gerontology ResearchFrench-language works237,207