“Get the DNR”: residents’ perceptions of goals of care conversations before and after an e-learning module
Bibliographic record
Abstract
Background: Residents frequently lead goals of care (GoC) conversations with patients and families to explore patient values and preferences and to establish patient-centered care plans. However, previous work has shown that the hidden curriculum may promote physician-driven agendas and poor communication in these discussions. We previously developed an online learning (e-learning) module that teaches a patient-centered approach to GoC conversations. We sought to explore residents' experiences and how the module might counteract the impact of the hidden curriculum on residents' perceptions and approaches to GoC conversations. Methods: Eleven first-year internal medicine residents from the University of Toronto underwent semi-structured interviews before and after completing the module. Themes were identified using principles of constructivist grounded theory. Results: Prior to module completion, residents described institutional and hierarchical pressures to "get the DNR" (Do-Not-Resuscitate), leading to physician-centered GoC conversations focused on code status, documentation, and efficiency. Tensions between formal and hidden curricula led to emotional dissonance and distress. However, after module completion, residents described new patient-centered conceptualizations and approaches to GoC conversations, feeling empowered to challenge physician-driven agendas. This shift was driven by greater alignment of the new approach with their internalized ethical values, greater tolerance of uncertainty and complexity in GoC decisions, and improved clinical encounters in practice. Conclusion: An e-learning module focused on teaching an evidence-based, patient-centered approach to GoC conversations appeared to promote a shift in residents' perspectives and approaches that may indirectly mitigate the influence of the hidden curriculum, with the potential to improve quality of communication and care.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".