MétaCan
Menu
Back to cohort
Record W3115835133 · doi:10.1093/geroni/igaa057.2718

Institutional and Cultural Barriers to ACP: Staff Perspectives

2020· article· en· W3115835133 on OpenAlexaff
Gloria Gutman, Brian de Vries, Helen Kwan, Katrina Jang, Shimae Soheilipour

Bibliographic record

VenueInnovation in Aging · 2020
Typearticle
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsMisinformationFocus groupOpenness to experienceNursingPsychologyAged careEthnic groupMedical educationMedicineSocial psychologyPolitical scienceSociology

Abstract

fetched live from OpenAlex

Abstract This study explored staff knowledge and engagement in assisting residents/families with ACP. Focus groups were conducted at two long-term care homes, one Exclusively Chinese (EC; n = 25); one Multi-Ethnic (ME; n = 41). In each, separate focus groups were held with registered staff, care aides, and support staff who also completed brief surveys providing socio-demographic data and information about their training and experience with ACP. Perceived barriers to engagement in ACP included limited knowledge and inadequate training in facilitating ACP, cognitive impairment of residents, language barriers, lack of openness to discussing ACP, family expectations and misinformation. EC staff also considered cultural and religious beliefs as one of the main barriers to engaging in ACP both for residents and families; staff at ME focused more on timing and the role of family. Support staff and care aides did not perceive ACP as within their scope of practice, deferring to nurses.

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)0.009
metaresearch head score (Gemma)0.019
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 score0.021
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.003
Scholarly communication0.0030.002
Open science0.0010.004
Research integrity0.0010.001
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 head0.025
GPT teacher head0.308
Teacher spread0.284 · 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
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueInnovation in AgingSame topicIntensive Care Unit Cognitive DisordersFrench-language works237,207