Breaking silence: a survey of barriers to goals of care discussions from the perspective of oncology practitioners
Bibliographic record
Abstract
BACKGROUND: Cancer is the leading cause of death in the developed world, and yet healthcare practitioners infrequently discuss goals of care (GoC) with hospitalized cancer patients. We sought to identify barriers to GoC discussions from the perspectives of staff oncologists, oncology residents, and oncology nurses. METHODS: This was a single center survey of staff oncologists, oncology residents, and inpatient oncology nurses. Barriers to GoC discussions were assessed on a 7-point Likert scale (1 = extremely unimportant; 7 = extremely important). RESULTS: Between July 2013 and May 2014, of 185 eligible oncology clinicians, 30 staff oncologists, 10 oncology residents, and 28 oncology nurses returned surveys (response rate of 37%). The most important barriers to GoC discussions were patient and family factors. They included family members' difficulty accepting poor prognoses (mean score 5.9, 95% CI [5.7, 6.2]), lack of family agreement in the goals of care (mean score 5.8, 95% CI [5.5, 6.1]), difficulty understanding the limitations of life-sustaining treatments (mean score 5.8, 95% CI [5.6, 6.1]), lack of patients' capacity to make goals of care decisions (mean score 5.7, 95% CI [5.5, 6.0]), and language barriers (mean score 5.7, 95% CI [5.4, 5.9]). Participants viewed system factors and healthcare provider factors as less important barriers. CONCLUSIONS: Oncology practitioners perceive patient and family factors as the most limiting barriers to GoC discussions. Our findings underscore the need for oncology clinicians to be equipped with strong communication skills to help patients and families navigate GoC discussions.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".