Residents’ reflections on end-of-life conversations: how a palliative care clinical rotation creates meaningful learning opportunities
Bibliographic record
Abstract
BACKGROUND: Good communication at the end-of-life is important for patient outcomes and satisfaction. However, many healthcare providers are hesitant to engage in these conversations due to inadequate training. Classroom and bedside palliative care training have been effective in improving resident communication with patients at the end-of-life, yet the educational mechanisms that promote development remain uncharacterized. The purpose of this study was to better understand how family medicine residents are trained to have goals of care (GOC) conversations during a clinical rotation at a specialized palliative care center. METHODS: We conducted 15 semi-structured interviews with first- and second-year family medicine residents who completed a 4-week palliative care rotation at a specialized palliative care center between July 2013 and June 2014. We asked residents about their educational experiences during the rotation, which included both inpatient and home-visit experiences. Using thematic analysis, we identified and described recurrent experiences reported by participants related to their exposure to and development of GOC conversations. RESULTS: Participants reported feeling more comfortable approaching GOC conversations at the end of the rotation. Residents noted two elements of their training experience that may have facilitated this development: a constructive learning environment that included time and support during and after GOC conversations, and learning activities that provided various levels of supervision and independence. CONCLUSIONS: A palliative care rotation may be an optimal environment for developing GOC conversation skills. Direct observation of learners and fewer time pressures provide important opportunities for mentoring, support, feedback and reflection, which were all noted to facilitate GOC conversation development.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".