Using the Serious Illness Conversation Guide (SICG) in Patients with Hematological Malignancies As a Tool to Address Their Perspectives on Palliative Care
Bibliographic record
Abstract
Introduction Early involvement by palliative care (PC) has been shown to positively affect the physical and psychological wellbeing of patients with hematological malignancies. Despite this knowledge, PC involvement is rare in this patient population and if present, often occurs too late. Many barriers to the integration of palliative care have been proposed in the literature, including illness-specific, cultural and systems barriers. Moving knowledge into practice is a complex process dependent on many variables. The field of knowledge translation (KT) makes use of conceptual frameworks to better understand the influence and relationship between these variables to improve the implementation of knowledge into practice. Understanding key stakeholder's, which include hematologists and patients, is an important step in KT. Many studies address physician's perspective, but a rare few have explored patient perspectives on palliative care. Methods We are conducting a prospective, mixed method, study assessing the Serious Illness Conversation Guide (SICG) in patients with blood cancers identified at risk of dying in the next 12 months . The study was conducted at a university-affiliated tertiary cancer center (Segal Cancer Center in Montreal Canada). 12 patients agreed to participate in the completed qualitative component of the study which involved face to face, semi-structured interviews two to four weeks following administration of the SICG. Interviews were conducted individually, audio-recorded, transcribed verbatim, and analyzed by three reviewers using thematic analysis. Results Participants (n=12) had a mean age of 68 [42-84]; 66.7% (n = 8) identified as male and 33.3% (n = 4) as female; 75% (n = 9) were Caucasian, 16.7% (n = 2) North African and 8.3% (n = 1) Black; 50% (n = 6) had acute leukemia, 25% (n = 3) lymphoma/CLL, 16.7% ( n = 2) MDS and 8.3% (n = 1) multiple myeloma. Themes identified relating to PC were: 1) thoughts on mortality 2) perspectives on palliative care, 3) perspectives on medical aid in dying (MAID) and 4) fear of abandonment. Most patients have thought of their death and seem aware of their unpredictable prognosis. What scared them most, even those that have accepted their mortality, is not the fact that they will die but the fear of not knowing how they will die (symptoms, pain, suffering). None of the patients expressing this fear addressed it with either their hematologist or PC provider. Interestingly, patients expressed the need for their treating physician to validate their impending death. Misconceptions relating to PC were common. One patient described it as “quiet euthanasia”. There were many negative associations to PC including associations with imminent death, suffering and loss of quality of life. The few patients who had positive opinions of PC had previous direct or indirect contact with PC. MAID was seen more positively than PC. It was described, especially by those with misconceptions of PC, as a more preferable way to die associated with less suffering. Finally, patients expressed the desire to not be abandoned by their hematologist if PC was involved. Conclusion The SICG facilitated the discussion of difficult topics such as death and PC. The insight gained in understanding patient's perspectives on mortality and PC will help address the barriers identified and develop effective interventions to facilitate the early involvement of PC in the care of patients with hematologic malignancies and hopefully bridge the KT gap.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".