Medical and Nursing Students' Past Personal Loss Experiences Influence Their Anticipated Future Professional Loss Reactions
Bibliographic record
Abstract
This study explores how medical and nursing students' personal bereavement experiences influence anticipated professional bereavement reactions and the mediating role of death attitudes. An online cross sectional survey was conducted with 405 medical and nursing students from Mainland China. Data included basic demographics, personal bereavement experiences within the past 2 years, death attitudes, and anticipated short-term professional bereavement reactions (anticipated SBR). Comparisons were made between students with and without bereaved experiences, and mediation pathways of "bereavement experience-death attitude-anticipated SBR" were analyzed. Students with personal bereavement scored significantly higher on overall anticipated SBR and its factors (frustration & trauma, guilt, grief, and moved). Approach acceptance partially mediated the relationship between bereavement and overall SBR, specifically influencing guilt and grief but not frustration & trauma or moved. Previous grief experiences impact anticipations about subsequent ones across different types of loss. Personal loss experiences within the past 2 years influence medical and nursing students' anticipated professional bereavement reactions by shaping death attitudes, and approach acceptance specifically mediates the relationship with expected guilt and grief.
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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".