[ONCOLOGISTS' APPROACHES AND BARRIERS FOR DISCUSSING ADVANCED CARE PLANNING WITH SEVERELY ILL CANCER PATIENTS].
Bibliographic record
Abstract
BACKGROUND: Earlier goals of care (GOC) discussions in patients with advanced cancer are associated with less aggressive end-of-life (EOL) care and with better quality of life near death. Despite that, these discussions do not always occur between oncologists and their patients. OBJECTIVES: To evaluate oncologists' agendas concerning EOL discussions and advanced directive (AD), and to identify barriers to these discussions. METHODS: The study included oncologists from Israeli hospitals who were asked to complete a questionnaire in order to assess barriers to EOL conversations. The questionnaire was adapted from Canadian research among clinicians in medical wards. Participants were asked to rank the importance of the various barriers. RESULTS: The questionnaires were completed by 84 physicians. Most physicians in this group (97%) thought it was important to have discussions on GOC with the patient, and 67% thought it was important that the patient would sign an AD form. Respondents perceived patient and family-related factors as the most important barriers. Of these, the most important were the patients' and patients' families difficulty accepting their poor prognosis, rated as important by 90% and 78% respectively, and the patients' difficulty understanding the limitations and complications of life-sustaining treatments, rated as an important by 81% of respondents. While physicians and system factors were ranked lower than patient-related factors, time limitations and desire to maintain hope were also considered important, 80% and 74% respectively. CONCLUSIONS: Oncologists ranked patient and family-related factors as the most important barriers to GOC discussions. Time limitations and the desire to maintain hope were also considered important. DISCUSSION: Further work is required to assess patient preferences and perceptions and to develop targeted interventions.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".