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Perspectives of community dwelling octogenarians about the use of life-sustaining technologies for their future care

2012· article· en· W2326670333 on OpenAlexaff
Jennifer Kryworuchko, Patricia H. Strachan, Rebecca Heyland, Daren K. Heyland

Bibliographic record

VenueBMJ Supportive & Palliative Care · 2012
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsQueen's UniversityMcMaster UniversityKingston General HospitalUniversity of Saskatchewan
Fundersnot available
KeywordsPreparednessAdvance care planningPreferencePsychologyPerceptionFocus groupQualitative researchHealth carePublic relationsNursingSociologyMedicinePalliative carePolitical scienceBusinessMarketing

Abstract

fetched live from OpenAlex

To better inform advance care planning initiatives, we conducted a qualitative study of community dwelling people aged 80 or more to enrich our understanding of their perspectives about the use of life-sustaining technologies in relation to their future care. In a semi-structured interview, 14 participants were asked what kind of health care they would want if they became seriously ill, so ill that they might die. We specifically probed their preferences for life-sustaining technologies and circumstances in which they had shared these preferences. Transcripts and field notes were analysed to categorise data in themes and subthemes. Participants represented octogenarians with variable levels of decision preparedness and included people who preferred a range of involvement in decision-making from passive to active consumers. Three overarching themes included: (1) their faith in others to do the right thing, (2) their focus on managing certainties, and (3) the role that perception of prolonging agony or alleviating suffering played in their preference for life-sustaining technologies. Few participants had considered that decisions would need to be made about life-sustaining technologies during the dying process and therefore had never considered the need to discuss their preferences to facilitate decisions in future. A key barrier to engaging some octogenarians may be their perception that there was no decision to be made since “they'll take care of me”. Insights gained from this study highlight the sort of “active engagement” strategies needed to improve advance care planning and 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 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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.398
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.240
GPT teacher head0.437
Teacher spread0.196 · 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 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
Published2012
Admission routes1
Has abstractyes

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