Opportunity is the Greatest Barrier to Providing Palliative Care to Advanced Colorectal Cancer Patients: A Survey of Oncology Clinicians
Bibliographic record
Abstract
Palliative care (pc) is part of the recommended standard of care for patients with advanced cancer. Nevertheless, delivery of pc is inconsistent. Patients who could benefit from pc services are often referred late-or not at all. In planning for improvements to oncology pc practice in our health care system, we sought to identify barriers to the provision of earlier pc, as perceived by health care providers managing patients with metastatic colorectal cancer (mcrc). We used the Michie Theoretical Domains Framework (tdf) and Behaviour Change Wheel (bcw), together with knowledge of previously identified barriers, to develop a 31-question survey. The survey was distributed by e-mail to mcrc health care providers, including physicians, nurses, and allied staff. Responses were obtained from 57 providers (40% response rate). The most frequently cited barriers were opportunity-related-specifically, lack of time, of clinic space for consultations, and of access to specialist pc staff or services. Qualitative responses revealed that resource limitations varied by cancer centre location. In urban centres, time and space were key barriers. In rural areas, access to specialist pc was the main limiter. Self-perceived capability to manage pc needs was a barrier for 40% of physicians and 30% of nurses. Motivation was the greatest facilitator, with 89% of clinicians perceiving that patients benefit from pc. Based on the Michie tdf and bcw model, interventions that best address the identified barriers are enablement and environmental restructuring. Those findings are informing the development of an intervention plan to improve oncology pc practices in a publicly funded health care system.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".