An exploration of contextual dimensions impacting goals of care conversations in postgraduate medical education
Bibliographic record
Abstract
BACKGROUND: Postgraduate medical trainees are not well prepared difficult conversations about goals of care with patients and families in the acute care clinical setting. While contextual nuances within the workplace can impact communication, research to date has largely focused on individual communication skills. Our objective was to explore contextual factors that influence conversations between trainees and patients/families about goals of care in the acute care setting. METHODS: We conducted an exploratory qualitative study involving five focus groups with Internal Medicine trainees (n = 20) and a series of interviews with clinical faculty (n = 11) within a single Canadian centre. Thematic framework analysis was applied to categorize the data and identify themes and subthemes. RESULTS: Challenges and factors enabling goals of care conversations emerged within individual, interpersonal and system dimensions. Challenges included inadequate preparation for these conversations, disconnection between trainees, faculty and patients, policies around documentation, the structure of postgraduate medical education, and resource limitations; these challenges led to missed opportunities, uncertainty and emotional distress. Enabling factors were awareness of the importance of goals of care conversations, support in these discussions, collaboration with colleagues, and educational initiatives enabling skill development; these factors have resulted in learning, appreciation, and an established foundation for future educational initiatives. CONCLUSIONS: Contextual factors impact how postgraduate medical trainees communicate with patients/families about goals of care. Attention to individual, interpersonal and system-related factors will be important in designing educational programs that help trainees develop the capacities needed for challenging conversations.
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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 | 0.016 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| 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".