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Record W2777805399 · doi:10.1177/2333721417747323

“Now I Don’t Have to Guess”: Using Pamphlets to Encourage Residents and Families/Friends to Engage in Advance Care Planning in Long-Term Care

2017· article· en· W2777805399 on OpenAlexafffund
Tamara Sussman, Sharon Kaasalainen, Matthew Bui, Noori Akhtar‐Danesh, Susan Mintzberg, Patricia H. Strachan

Bibliographic record

VenueGerontology and Geriatric Medicine · 2017
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsMcMaster UniversityMcGill University
FundersCanadian Frailty Network
KeywordsAdvance care planningFocus groupLong-term careHealth careReading (process)NursingMedicinePsychologyPalliative careGerontologySociologyPolitical science

Abstract

fetched live from OpenAlex

Objective: This article explores whether access to illness trajectory pamphlets for five conditions with high prevalence in long-term care (LTC) can encourage residents and families/friends to openly engage in advance care planning (ACP) discussions with one another and with health providers. Method: In all, 57 residents and families/friends in LTC completed surveys and 56 participated in seven focus groups that explored whether the pamphlets supported ACP engagement. Results: Survey results suggested that access to pamphlets encouraged residents and families/friends to reflect on future care (48/57, 84%), clarified what questions to ask (40/57, 70%), and increased comfort in talking about end of life (EOL) care (36/57, 63%). Discussions between relatives and friends/families (32/57, 56%) or with health providers (21/57, 37%) were less common. Focus group deliberations illuminated that while reading illness-specific information was validating, a tendency to protect one another from an emotional topic, prevented residents and families/friends from conversing with one another about EOL issues. Discussion: Having access to pamphlets with information about EOL care provides important and welcome opportunities for reflection for both residents in LTC and their families/friends. Moving residents and families/friends from reflecting on issues to discussing them together could require staff support through planned care conferences or staff initiated conversations at the bedside.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score0.973

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.091
GPT teacher head0.443
Teacher spread0.352 · 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.

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

Citations22
Published2017
Admission routes2
Has abstractyes

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