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Record W2580802654 · doi:10.1177/1742395316675023

Readiness to participate in advance care planning: A qualitative study of renal failure patients, families and healthcare providers

2017· article· en· W2580802654 on OpenAlexaffabout
Lauren A Hutchison, Donna S Raffin-Bouchal, Charlotte Ann Syme, Patricia Biondo, Jessica Simon

Bibliographic record

VenueChronic Illness · 2017
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsUniversity of Calgary
FundersHealth Research Board
KeywordsAdvance care planningCognitive reframingContext (archaeology)Health careMedicineNursingPsychologyPalliative careSocial psychologyPolitical science

Abstract

fetched live from OpenAlex

Objectives Advance care planning is the process by which people reflect upon their wishes and values for healthcare, discuss their choices with family and friends and document their wishes. Readiness represents a key predictor of advance care planning participation; however, the evidence for addressing readiness is scarce within the renal failure context. Our objectives were to assess readiness for advance care planning and barriers and facilitators to advance care planning uptake in a renal context. Methods Twenty-five participants (nine patients, nine clinicians and seven family members) were recruited from the Southern Alberta Renal Program. Semi-structured interviews were recorded, transcribed and then analyzed using interpretive description. Results Readiness for advance care planning was driven by individual values perceived by a collaborative encounter between clinicians and patients/families. If advance care planning is not valued, then patients/families and clinicians are not ready to initiate the process. Patients and clinicians are delaying conversations until "illness burden necessitates," so there is little "advance" care planning, only care planning in-the-moment closer to the end of life. Discussion The value of advance care planning in collaboration with clinicians, patients and their surrogates needs reframing as an ongoing process early in the patient's illness trajectory, distinguished from end-of-life decision making.

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)0.018
metaresearch head score (Gemma)0.026
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 score0.024
Threshold uncertainty score0.093

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.026
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0130.010
Scholarly communication0.0040.004
Open science0.0020.005
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.110
GPT teacher head0.477
Teacher spread0.368 · 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

Citations43
Published2017
Admission routes2
Has abstractyes

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