Patient, family member, and clinician perspectives on advance care planning (ACP) in hematology and hematopoietic stem cell transplantation (HSCT).
Bibliographic record
Abstract
7 Background: Studies have found that ACP engagement remains low in patients undergoing HSCT in spite of the high risks of treatment-related morbidity and mortality. Methods: This qualitative study used Thorne’s Interpretive Description methodology. Participants were accrued from hematological malignancy outpatient clinics at a tertiary oncology center and underwent audio-recorded semi-structured interviews. Analysis involved meticulous review of interview transcripts. The constant comparative method was utilized; data collection occurred concurrently with analysis until saturation of themes was achieved. Results: The study involved 6 patients, 5 family members and 8 clinicians (physicians, nurses, social worker). Participants thought that ACP was both acceptable and important yet many had not engaged in ACP. Perceived barriers to ACP included: system-related barriers such as lack of process for ACP, lack of time and/or resources; patient-related factors such as lack of understanding of disease, prognosis and/or expectations of HSCT, lack of patient/family understanding of ACP, a desire to ‘focus on positives’; and disease/treatment-related factors such as unpredictability of the disease and treatment trajectories in hematology and HSCT. Potential facilitators identified by participants included: integrating ACP as part of routine HSCT care, involving the multidisciplinary team in ACP, and introducing ACP early and revisiting frequently. Conclusions: This study revealed various barriers and facilitators related to participation in ACP. We are using the results of this study to inform and tailor interventions on ACP at our center. Introducing ACP as part of standard care in HSCT and providing ongoing facilitation of ACP, including discussion of disease and treatment expectations at the outset, and when complications arise, may assist patients and families in recognizing how ACP fits into their care. Given the inherent unpredictability in this population, we suggest revisiting ACP frequently to optimize patient experience and ensure patients and family members are aware of other treatment options including supportive and palliative care.
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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 | 0.016 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 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".