Barriers and facilitators for goals of care discussions between residents and hospitalised patients
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".