Perceived Barriers to Goals of Care Discussions With Patients With Advanced Cancer and Their Families in the Ambulatory Setting
Bibliographic record
Abstract
BACKGROUND: Earlier goals of care (GOC) discussions in patients with advanced cancer are associated with less aggressive end-of-life care including decreased use of medical technologies. Unfortunately, conversations often occur late in the disease trajectory when patients are acutely unwell. Here, we evaluate practitioner perspectives of patient, family, physician, and external barriers to early GOC discussions in the ambulatory oncology setting. METHODS: A previously published survey to assess barriers to GOC discussions among clinicians on inpatient medical wards was modified for the ambulatory oncology setting and distributed to oncologists from 12 centers in Ontario, Canada. Physicians were asked to rank the importance of various barriers to having GOC discussions (1 = extremely unimportant to 7 = extremely important). RESULTS: Questionnaires were completed by 30 (24%) of 127 physicians. Respondents perceived patient- and family-related factors as the most important barriers to GOC discussions. Of these, patient difficulty accepting prognosis or desire for aggressive treatment were perceived as most important. Patients' inflated expectation of treatment benefit was also considered an important barrier to discontinuing active cancer-directed therapy. While physician barriers were ranked lower than patient-related factors, clinicians' self-identified difficulty estimating prognosis and uncertainty regarding treatment benefits were also considered important. Patient's refusal for referral was the most highly rated barrier to early palliative care referral. Most respondents were nonetheless very or extremely willing to initiate (90%) or lead (87%) GOC discussions. CONCLUSION: Oncologists ranked patient- and family-related factors as the most important barriers to GOC discussions, while clinicians' self-identified difficulty estimating prognosis and uncertainty regarding treatment benefits were also considered important. Further work is required to assess patient preferences and perceptions and develop targeted interventions.
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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 | 0.002 | 0.013 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".