A cross-sectional examination of sudden-death bereavement in university students
Bibliographic record
Abstract
Background: This thesis addresses the topic of sudden death bereavement in university students. Sudden death bereavement is due to the sudden, unexpected loss of a loved one. It often occurs in a traumatic manner, which can contribute to adverse psychological and physical outcomes, including increased mortality risk. University students are a vulnerable population for poor mental health outcomes, and the sudden loss of a loved one is the most common traumatic event reported in this population. Therefore, we aim to add to the knowledge about the prevalence of this phenomenon in university students, outcomes following sudden bereavement, and factors associated with these outcomes. Method: Using a survey method that was administered online, introductory psychology students provided sociodemographic information and bereavement related information. They also completed questionnaires on mental health status, including depression, generalized anxiety, complicated grief, Post Traumatic Stress Disorder (PTSD), and alcohol use. They finally completed questionnaires on help-seeking behaviour and coping. Results: Results from our study indicated that the sudden loss of a loved one is highly prevalent among university students. Sudden death bereaved university students were more likely to experience negative outcomes, such as complicated grief, even when the time since death, relationship to the deceased, and closeness of the relationship were taken into account. Rumination and avoidance were associated with poorer mental health outcomes with regards to depression, generalized anxiety, suicidal ideation, and posttraumatic stress disorder. Sudden death bereaved students who had significant mental health concerns were more likely to seek help.
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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 | 0.004 |
| 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 | 0.000 | 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".