Knowledge and attitudes of medical students toward death: a cross-sectional comparative study between an Arab and a Western University
Bibliographic record
Abstract
BACKGROUND: Cultural factors influence attitudes toward death, and gender disparities are evident. Prior studies show that medical students have limited knowledge about death and are uncomfortable with it. Moreover, there is limited research that has examined factors that influence medical students' knowledge and attitudes toward death. OBJECTIVES: The objectives of the study were to compare cultural and gender differences in relation to knowledge and attitudes toward loss and grief and to screen for complicated grief among medical students at the Arabian Gulf University and the University of Toronto. METHODS: A cross-sectional study was disseminated to medical students at both universities in 2022. The variables in the survey included four parts: demographic characteristics of the participants, religious observance, history of encountering loss of a loved one, grief following loss, attitude toward death, and learning about how to deal with grief and death during medical school. The brief grief questionnaire and the death attitude profile-revised scales were used. RESULTS: The study sample consisted of 168 medical students, with 74.1% being female. Complicated grief scores were higher among Arabian Gulf University students (3.87 ± 2.39) than among University of Toronto students (2.00 ± 1.93) and were higher for participants with a higher degree of religious observance in both schools (p < 0.05). Death avoidance (p = 0.003), approach acceptance (p < 0.001), and escape acceptance (p = 0.038) domains were significantly higher among Arabian Gulf University students than among University of Toronto students. Almost three-quarters of University of Toronto students reported not being taught about grief, compared to 54% of Arabian Gulf University students. CONCLUSIONS: Arabian Gulf University medical students scored higher on complicated grief, most likely due to cultural and religious factors. Females at both institutions as well as those who indicated a higher level of religious observance reported higher scores of complicated grief. The study highlights how cultural and religious beliefs influence medical students' attitudes toward death and bereavement. It provides valuable insight into the knowledge and attitudes of medical students toward loss.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".