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Barriers and facilitators for goals of care discussions between residents and hospitalised patients

2016· article· en· W2495024261 on OpenAlexaff
Kalpa Shah, Marilyn Swinton, John J. You

Bibliographic record

VenuePostgraduate Medical Journal · 2016
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsMcMaster UniversityUniversity of Toronto
Fundersnot available
KeywordsThematic analysisMedicineQualitative researchNursingMEDLINEFamily medicineService (business)

Abstract

fetched live from OpenAlex

PURPOSE: To observe how residents are engaging in goals of care discussions with patients and identify thematic patterns that inhibited (barriers) and promoted discussion (facilitators) about goals of care. DESIGN: Admission encounters between residents and patients admitted to a tertiary care academic hospital were recorded and analysed using a qualitative descriptive method. Patients included in the study were individuals over the age of 65 being admitted to the internal medicine service. Residents were eligible if they were trainees in family medicine, emergency medicine, general surgery or internal medicine who were on call for the inpatient medicine rotation. RESULTS: A total of 15 resident-patient encounters were recorded and analysed, of which 12 encounters included a goals of care discussion. Barriers to goals of care discussions were due to missed opportunities to clarify patient's preferences for life-sustaining treatment and missed opportunities to engage the patient in further discussion. Facilitators to goals of care discussions were use of simple language and exploration of patient's previous experiences with life-sustaining treatment. CONCLUSIONS: Asking about patients' previous experiences with life support can be an effective strategy to gauge the patient's understanding and goals of care preferences. This knowledge can improve residents' skill in communicating with their patients about goals of care and inform future education initiatives.

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)0.001
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.075
GPT teacher head0.404
Teacher spread0.330 · 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 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

Citations24
Published2016
Admission routes1
Has abstractyes

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