“What are you afraid of?” A mixed methods exploration of serious illness communication with oncology patients on general internal medicine wards in Canada
Bibliographic record
Abstract
Abstract Background While emphasized as a key aspect of patient-centered care, little is known about the delivery and documentation of serious illness conversations (SIC) for patients with cancer admitted on general internal medicine (GIM) wards. Objectives To characterize the documentation and experiences of SIC from the perspectives of patients and clinicians on GIM wards. Methods This mixed methods quality improvement project gathered data from GIM wards using a: (1) retrospective review of electronic medical record data of hospitalized patients with cancer, (2) survey of physicians, residents, nurses, and allied health clinicians regarding their clinical practice, and (3) semi-structured interviews with hospitalized patients/caregivers. Results The charts of 101 patients were reviewed: 85.2% had a documented code status, while less than half (46.5%) had documentation of a conversation with a clinician addressing hopes, concerns or values. Ninety-seven clinicians completed the survey and reported variable levels of documentation of SICs. Clinician-identified barriers to serious illness conversations included language barriers, prognostic uncertainty, and lack of time. The 21 patients/caregivers who were interviewed reported a lack of focus on values in their discussions with clinicians, and a desire for their clinician to tailor these discussions to their individual needs. Conclusion In patients with cancer admitted to GIM wards, code status was frequently documented, whereas values-based components of these SICs were less frequently recorded Our results represent an opportunity to improve both the delivery and documentation of more holistic, person-centered aspects of serious illness conversations as a means to drive goal-concordant care through targeted clinical and educational interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.030 | 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 teacher head, 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".