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Record W4405960840 · doi:10.1093/geroni/igae098.2388

FAMILY PERCEPTIONS ABOUT ENGAGING IN PALLIATIVE CARE CONFERENCES FOR THEIR LOVED ONES WHO LIVE IN LONG-TERM CARE

2024· article· en· W4405960840 on OpenAlexaffabout
Sharon Kaasalainen, Genevieve Thompson, Abigail Wickson‐Griffiths, Paulette V. Hunter, Lynn McCleary, Tamara Sussman

Bibliographic record

VenueInnovation in Aging · 2024
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsUniversity of ManitobaBrock UniversityUniversity of SaskatchewanMcGill UniversityUniversity of ReginaMcMaster University
Fundersnot available
KeywordsTerm (time)PerceptionPsychologyPalliative careNursingLong-term careMedicineNeuroscience

Abstract

fetched live from OpenAlex

Abstract Despite the high mortality rates in long term care (LTC), most LTC homes do not have a formalized palliative program. Communication with family members or care partners is critical to prepare them for end-of-life and promotes shared decision-making. The aim of this study was to explore family perceptions about engaging in Palliative Care Conferences (PCCs) which were held for their loved one who was dying in LTC. This study was conducted in four provinces in Canada (Ontario, Manitoba, Saskatchewan, Alberta) and utilized a qualitative description design. Of the 36 family members who were interviewed, 12 were bereaved and 24 were non-bereaved. Overall, they felt that PCCs provided a venue for learning and appreciated the interdisciplinary approach. It gave them some time and space to develop stronger, more caring relationships with staff that sometimes was difficult to do during normal day-to-day activities. Family stated the PCCs were informative and promoted quality communication. They also described how the timing of PCCs and other discussions is important so they don’t feel rushed and can ‘go at their own pace’, allowing them to receive the information that they need when they are ready for it. PCCs appear to feasible and support a family-centered approach to care, which relies on strong communication. Future work needs to include a more rigorous evaluation that builds PCCs into everyday practice.

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)0.006
metaresearch head score (Gemma)0.015
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: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.092

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0050.003
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.143
GPT teacher head0.437
Teacher spread0.294 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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