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Record W3145481528 · doi:10.1097/njh.0000000000000760

Simulated Learning Experience in Advance Care Planning Conversations

2021· article· en· W3145481528 on OpenAlexaff
Brandy Ellis, Sheryl Winn, Deborah MacMillan, Kelly A. Bouthillet, Cynthia Vorpahl Purcell

Bibliographic record

VenueJournal of Hospice and Palliative Nursing · 2021
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsGeorgian College
Fundersnot available
KeywordsAdvance care planningConversationAutonomyNursingPalliative careMedicineFidelityPsychologyProtocol (science)Confidence intervalSimulated patientFamily medicineMedical educationAlternative medicineComputer scienceInternal medicine

Abstract

fetched live from OpenAlex

The Centers for Disease Control and Prevention reports that one-third of all Americans have not done any advance care planning, which is essential for maintaining autonomy when people are no longer able to communicate wishes directly at the end of life. The Institute of Medicine reports that providers have limited training in communication techniques and low confidence in holding advance care planning conversations. Forty-four family nurse practitioner students received classroom communication training using evidence-based end-of-life nursing education consortium materials and role-play opportunities. One month later, students participated in a palliative care simulation experience in a high-fidelity simulated medical office environment with standardized patient. The family practice nurse practitioner students were required to communicate bad news of a poor diagnosis to a family member and begin the advance care planning conversation using the SPIKES protocol. Findings include a significant increase in postintervention advance care planning communication knowledge scores and self-confidence in holding these conversations (mean, 30.55 [SD, 2.992]; 95% confidence interval, 29.64-31.46; median, 31; range, 11-35) and satisfaction with the learning experience (mean, 18.14 [SD, 2.11]; 95% confidence interval, 17.50-18.78; median, 19; with range between 13 and 20).

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.375
Threshold uncertainty score0.341

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.119
GPT teacher head0.474
Teacher spread0.355 · 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

Citations13
Published2021
Admission routes1
Has abstractyes

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