Personality characteristics of physicians and end-of-life decisions in Russia.
Bibliographic record
Abstract
OBJECTIVE: To explore the relationships between personality characteristics, underlying attitudes, and treatment decisions for severely ill elderly patients in a sample of Russian doctors. DESIGN: Survey. SETTING: Group sessions during meetings or individual presentations of questionnaire. SUBJECTS: A convenience sample of 231 physicians from the Archangelsk region in northern Russia who frequently encounter treatment situations with incompetent elderly patients. MEASUREMENTS: Temperament and Character Inventory (Cloninger et al, 1994) for assessing personality dimensions. The questionnaire on decision-making is based on the original developed by Molloy and coworkers from McMaster University in Canada. In a case-vignette, the condition of an 82-year-old man with acute gastrointestinal bleeding is described comprehensively in combination with 3 different levels of information about the patient wishes (no information, DNR order, advance directive). Questions about importance of legal concerns, patient and family wishes, hospital costs, patient's age and level of dementia, and physician's religion for the doctor's decision-making are added. MAIN RESULTS: No significant relationship was found between chosen treatment options and personality traits in any of the 3 situations. However, personality characteristics such as self-directedness, cooperativeness, and self-transcendence, in particular, show significant relationships with attitudes underlying these decisions. CONCLUSIONS: Physicians should be trained to improve their cooperative abilities in the treatment of severely ill elderly patients to be better prepared for their decision-making and coping concerning end-of-life decisions and the use of do-not-resuscitate orders and advance directives. Ethical values in clinical practice, especially patient autonomy, should be addressed during the early stage of the medical curriculum.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".