Patient & Caregiver Experiences: Qualitative Study Comparison Before and After Implementation of Early Palliative Care for Advanced Colorectal Cancer
Bibliographic record
Abstract
BACKGROUND: The Palliative Care Early and Systematic (PaCES) program implemented an early palliative care pathway for advanced colorectal cancer patients in January 2019, to increase specialist palliative care consultation and palliative homecare referrals more than three months before death. This study aimed to understand the experience of patients with advanced colorectal cancer and family caregivers who received early palliative care supports from a specialist palliative care nurse and compared those experiences with participants who experienced standard oncology care prior to implementation of early palliative care. METHODS: This was a qualitative and patient-oriented study. We conducted semi-structured telephone interviews with two cohorts of patients with advanced colorectal cancer before and after implementation of an early palliative care pathway. We conducted a thematic analysis of the transcripts guided by a Person-Centred Care Framework. RESULTS: Seven patients living with advanced colorectal cancer and five family caregivers who received early palliative care supports expressed that visits from their early palliative care nurse was helpful, improved their understanding of palliative care, and improved their care. Four main themes shaped their experience of early palliative care: care coordination, perception of palliative care & advance care planning, coping with advanced cancer, and patient and family engagement. These findings were compared with experiences of 15 patients and seven caregivers prior to pathway implementation. CONCLUSION: An early palliative care pathway can improve advanced cancer care, and improve understanding and acceptance of early palliative care. This work was conducted in the context of colorectal cancer but may have relevance for the care of other advanced cancers.
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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) | 0.000 | 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".