Palliative Care Early and Systematic (PaCES): A survey of colorectal cancer clinicians of barriers and facilitators to early palliative care integration.
Bibliographic record
Abstract
128 Background: Health systems struggle to systematically provide early and integrated palliative care (PC) to cancer patients. In Alberta, Canada, the PaCES (Palliative Care Early and Systematic) project is addressing this need by building an early PC pathway for advanced colorectal cancer (CRC) patients. As part of this work, we aimed to characterize barriers and facilitators to: a) providing primary palliative care to CRC patients; b) referring CRC patients for PC specialist consults; and c) working with specialist PC and home care services. Methods: This observational, knowledge translation questionnaire study collected both quantitative and open-ended responses from physicians, nurses and other allied health care professionals (HCP) working with advanced CRC patients in Alberta, Canada. Survey questions and format were informed by Michie’s Theoretical Domains Framework. The strength of this framework is that, in addition to identifying specific barriers to delivering early PC, it also maps out suggested solutions to addressing these barriers. Results: The survey response rate was 43% (65/150). 89% of the respondents were physicians or nurses with Medical Oncology as the primary discipline. Time and competing priorities were the biggest barriers to HCPs addressing patients PC needs (65%). Next, role confusion when working with PC teams, and lack of clear process for executing new orders when patients are at home, were identified by over 55% of HCPs as a barrier. Other aspects of working with PC teams, including, lack of standardized communication processes and inadequate documentation processes, were barriers as perceived by over 45% of HCPs. Over 90% of HCPs surveyed believe earlier PC is likely to benefit their patients, and would recommend an earlier PC pathway to their patients. Conclusions: In Alberta, the main barriers to early PC integration are no longer attitudinal, with over 90% believing that early PC is beneficial for their patients. Time needed to address PC needs in a busy oncology clinic is a barrier. A pathway to improve clarity around referral processes and role confusion when working with PC teams will be developed to address these barriers.
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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 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".