Conducting Goals of Care Conversations: Lessons From the COVID-19 Pandemic
Bibliographic record
Abstract
Objective: Internal medicine (IM) residents discuss a patient's goals of care (GOC) as part of their initial consultation. Residents have described inexperience, general discomfort, limited formal teaching, and prognostic uncertainty as barriers to effective GOC conversations. The early COVID-19 pandemic resulted in rapid changes to the healthcare system on the individual, patient, and systemic level that might exacerbate and/or introduce new barriers to IM residents’ GOC conversations. This qualitative study examines how the early COVID-19 pandemic challenged IM residents’ ability to have effective GOC conversations. Methods: Using a constructivist grounded theory approach, participants (n=11) completed a semi-structured interview. Data collection and analysis occurred simultaneously using an open coding, constant comparison process. Interviews were completed until no new themes were identified. Results: Residents self-described their GOC conversations in 5 steps: normalization of the conversation, introduction of expected clinical course, discussion of possible care plans, exploration of the patient's values, and occasionally providing a recommendation. Residents described limited structured teaching around GOC conversations and instead relied on observed role-modelling and self-practice to hone their skillset. Residents described an increased sense of urgency to have GOC conversations due to the uncertainty of clinical course and potential for rapid deterioration of patients with COVID-19. Residents identified restrictive visitor policies as a significant barrier that contributed to feelings of dehumanization. Residents felt that these limitations affected their GOC conversations and potentially resulted in discordant care plans which contributed to moral distress. Conclusion: The early COVID-19 pandemic resulted in several barriers that challenged residents’ ability to conduct effective GOC conversations. This is on the background of previously reported discomfort and limited formal training in conducting GOC conversations. Based on our findings, we present a conceptual model involving teaching validated GOC frameworks, positive role-modelling, and experiential learning to support GOC conversation education in post-graduate medical education.
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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 | 0.027 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.011 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.005 | 0.009 |
| 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".