Educating for Compassion: Detoxifying Death for Physicians' CME Curriculum
Bibliographic record
Abstract
The physician can play a leadership role in detoxifying death for the patient, the family, and the professional staff. However, physicians commonly manifest avoid ance and other dysfunctional behaviors when dealing with death. Dysfunctional physician behaviors around death are not rooted in cognitive understanding and are resistant to conventional didactic approaches (SUPPORT Principal Investigators, 1995). Physician responses to end-of-life patients are affected by the power of childhood memories, personal beliefs, attitudes and unexamined conclusions drawn from medical training. To affect behavioral change, a curriculum was developed that creates an intensive community of inquiry among physician peers to examine existing beliefs in depth and to motivate, validate, and support new behaviors. The curriculum on Detoxifying Death for Physicians was implemented 12 times over a three-year period. Physicians from the United States and Canada, representing a wide range of specialties and practice styles, participated in the curriculum. Participants reported significant attitudinal and behavioral change in the direction of increased comfort in caring for patients at the end of life and in discussing death and dying with colleagues. The Institute for the Study of Health and Illness's (ISHI) experience with this multimodal curriculum suggests that it is possible for mature physicians to significantly alter their attitudes and behaviors toward end-of-life patients through participation in an intensive educational process. It also suggests that change may be a function of the depth and integrity of the educational process rather than the length of exposure to the curriculum.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".