Oncologists' Strategies and Barriers to Effective Communication About the End of Life
Bibliographic record
Abstract
PURPOSE: Communicating about the end of life with patients has been reported as one of the most difficult and stressful part of the work of oncologists. Despite this fact, oncologists receive little training in this area, and many do not communicate effectively with patients. The purpose of this analysis, part of a larger study examining oncologists' experiences of patient loss, was to explore oncologists' communication strategies and communication barriers when discussing end-of-life issues with patients. METHODS: Twenty oncologists were interviewed at three hospitals about their communication strategies on end-of-life issues with patients. The data were analyzed using the grounded theory method. RESULTS: The findings revealed the strategies to effective communication about the end of life included: being open and honest; having ongoing, early conversations; communicating about modifying treatment goals; and balancing hope and reality. Barriers to implementing these strategies fell broadly into three domains, including physician factors, patient factors, and institutional factors. Physician factors included difficulty with treatment and palliation, personal discomfort with death and dying, diffusion of responsibility among colleagues, using the "death-defying mode," lack of experience, and lack of mentorship. Patient factors included, patients and/or families being reluctant to talk about the end of life, language barriers, and younger age. Institutional factors included stigma around palliative care, lack of protocol about end-of-life issues; and lack of training for oncologists on how to talk with patients about end-of-life issues. CONCLUSION: We conclude by drawing implications from our study and suggest that further research and intervention are necessary to aid oncologists in achieving effective communication about end-of-life issues.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".