Goals of care conversation teaching in residency – a cross-sectional survey of postgraduate program directors
Bibliographic record
Abstract
BACKGROUND: Residents are commonly involved in establishing goals of care for hospitalized patients. While education can improve the quality of these conversations, whether and how postgraduate training programs integrate such teaching into their curricula is not well established. The objective of this study was to characterize perceptions of current teaching and assessment of goals of care conversations, and program director interest in associated curricular integration. METHODS: An electronic survey was sent to all postgraduate program directors at the University of Calgary. Quantitative data was analyzed using descriptive statistics and qualitative comments were analyzed using thematic analysis. RESULTS: The survey response rate was 34% (22/64). Formal goals of care conversation teaching is incorporated into 63% of responding programs, and most commonly involves lectures. Informal teaching occurs in 86% of programs, involving discussion, direct observation and role modeling in the clinical setting. Seventy-three percent of programs assess goals of care conversation skills, mostly in the clinical setting through feedback. Program directors believe that over two-thirds of clinical faculty are prepared to teach goals of care conversations, and are interested in resources to teach and assess goals of care conversations. Themes that emerged include 1) general perceptions, 2) need for teaching, 3) ideas for teaching, and 4) assessment of goals of care conversations. CONCLUSIONS: The majority of residency training programs at the University of Calgary incorporate some goals of care conversation teaching and assessment into their curricula. Program directors are interested in resources to improve teaching and assessment of goals of care 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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".