Patient and caregiver experiences with advanced cancer care: a qualitative study informing the development of an early palliative care pathway
Bibliographic record
Abstract
BACKGROUND: Palliative care is an approach that improves the quality of life of patients and families facing challenges associated with life-threatening illness. In order to effectively deliver palliative care, patient and caregiver priorities need to be incorporated in advanced cancer care. AIM: This study identified experiences of patients living with advanced colorectal cancer and their caregivers to inform the development of an early palliative care pathway. DESIGN: Qualitative patient-oriented study. SETTINGS/PARTICIPANTS: Patients receiving care at two cancer centres were interviewed using semistructured telephone interviews to explore their experiences with cancer care services received prior to a new developed pathway. Interviews were transcribed verbatim, and the data were thematically analysed. RESULTS: From our study, we identified gaps in advanced cancer care that would benefit from an early palliative approach to care. 15 patients and 7 caregivers from Edmonton and Calgary were interviewed over the phone. Participants identified the following gaps in advanced cancer care: poor communication of diagnosis, lack of communication between healthcare providers, role and involvement of the family physician, lack of understanding of palliative care and advance care planning. CONCLUSIONS: Early palliative approaches to care should consider consistent and routine delivery of palliative care information, collaborations among different disciplines such as oncology, primary care and palliative care, and engagement of patients and family caregivers in the development of care pathways.
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".