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Record W4247905031 · doi:10.14283/jfa.2021.34

Symposia — Conferences — Oral Communications

2021· article· en· W4247905031 on OpenAlexaboutno aff

Bibliographic record

VenueThe Journal of Frailty & Aging · 2021
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsnot available
Fundersnot available
KeywordsSarcopeniaMedicineGerontologyInternal medicine

Abstract

fetched live from OpenAlex

Background: A growing number of older people reside in long-term care (LTC) homes.As they near the end-of-life, it is vital that LTC residents express their healthcare wishes though Advance Care Planning (ACP).Yet, ACP remains suboptimal and LTC residents often experience unmet needs and unnecessary hospital transfers.Objectives: We applied the Knowledge-To-Action framework to 1) identify shared barriers and solutions to improve the process of ACP and end-of-life care for LTC residents; 2) develop a standardized, scalable, and person-centered approach to ACP, and 3) evaluate this approach in a multicentre cluster randomized trial.Methods: We began in September 2017 with a 1-day workshop for 44 LTC stakeholders, including residents and families, from Manitoba, Alberta, and Ontario.Sessions were recorded and thematic analysis performed.An environmental scan was conducted to assess ACP practices in 38 LTC homes in participating provinces.Over the following 11 months, we developed the intervention to address weak links in ACP.From August 2018 to August 2020, we conducted an unblinded, cluster-randomized, mixed-methods trial in 29 LTC homes (15 intervention, 14 control) in these provinces to assess the impact of the intervention on ACP comprehensiveness and care and interventions at the end-of-life (ClinicalTrials.govNCT03649191).Results: ACP challenges include: 1) differing provincial ACP frameworks; 2) lacking clarity on substitute decision maker (SDM) identity and role; 3) failing to share sufficient information when residents formulate care wishes; and 4) failing to communicate during a health crisis.The environmental scan identified that most conduct ACP upon resident admission, with 90% repeating these when resident clinical status changes.Residents are often excluded from ACP. Physician involvement is often very limited, even in emergencies, leading to decisions counter to resident wishes.Recognizing the variability in physician involvement in ACP, we designed BABEL to be delivered by nurses.Requiring approximately 60 minutes, BABEL: 1) confirms the identity and role of the SDM; 2) prepares the SDM for medical emergencies; 3) explains the resident's clinical situation and prognosis; 4) ascertains the resident's decision-making philosophy; and 5) identifies preferred treatment options for medical emergencies most likely to be faced by that resident.Intervention materials include: a workbook, training tools for LTC staff, and knowledge tools for all stakeholders.The workbook contains carefully worded scripts to guide staff on helping residents and families navigate sensitive ACP.A preliminary discussion is intended to take place very soon after LTC admission, followed 2-8 weeks later by the Full BABEL Discussion, which is the core of the intervention.The trial recruited 713 LTC residents aged >= 65 years with an elevated risk of dying within the next year.The intervention significantly increased the comprehensiveness of ACP.Comfort in dying did not differ between groups.Antimicrobial use was significantly lower in intervention homes.Conclusions: The superior comprehensiveness of a person-centered BABEL ACP, codesigned with LTC stakeholders, underscores the importance of allowing adequate time for these discussions, to address all the important aspects of ACP, and may reduce unwanted interventions at the end of life.

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)0.005
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.663
Threshold uncertainty score0.945

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0050.003
Open science0.0020.005
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.3370.169

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 head0.164
GPT teacher head0.418
Teacher spread0.254 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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
Published2021
Admission routes1
Has abstractyes

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