Providing a safety net: a qualitative study on supporting medical students during goals of care discussions
Bibliographic record
Abstract
Background: Engaging in effective goals of care (GOC) discussions with patients is a critical skill for physicians. Medical students, however, often feel unprepared, unsupported and uncomfortable leading these conversations. We undertook the current study to explore senior medical students' experiences with GOC discussions during their clinical training, examining when and how (and when not and how not) GOC discussions impacted them and the influence of supervision on their GOC learning. Methods: We used qualitative interpretive description as our methodology. Fourth-year medical students at a single university were invited to voluntarily participate. Qualitative semi-structured interviews were used to foster rich discussion about students' GOC experiences during their clinical rotations. Data collection and analysis proceeded iteratively. All investigators participated in data analysis using an inductive, constant comparison approach to identify themes and subthemes. Results: Eleven fourth-year medical students were interviewed between 2021 and 2022. As students observed and conducted GOC conversations, participating in the conversation with a supportive clinical supervisor and with patients they knew appeared to positively influence students. In less supported environments, students experienced challenges during GOC conversations, reflected through perceived limitations and feelings of uncertainty. Supervisors also played an important role in helping students navigate a range of emotional responses to these conversations. Conclusions: Rather than shielding students from difficult conversations, supervisors can positively impact students' experiences by supporting them to engage in GOC discussions, thereby providing them with the skills to support patients through challenging moments.
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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 | 0.023 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.011 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".