Original Contribution Parental Bereavement After the Death of an Offspring in a Motor Vehicle Collision: A Population-based Study
Bibliographic record
Abstract
Motor vehicle collisions (MVCs) are the leading cause of death in young people in North America. The effects of such deaths on parents have not been systematically studied. Administrative data sets were used to identify all par-ents (n = 1,458) who had an offspring die in a MVC between 1996 and 2008 in the province of Manitoba, Canada. They were matched to general population control parents who had not had offspring die from any sudden cause during the study period. Generalized estimating equations were used to compare the rates of physician-diagnosed mental and physical disorders, social factors, and treatment utilization in the 2 parent groups in the 2 years before and after offspring death, with adjustment for confounding factors. The risk of depression among bereaved parents almost tripled (adjusted prevalence ratio = 2.85, 95 % confidence interval: 2.44, 3.33; P < 0.001) during the 2 years after death of an offspring. Significant increases in the risk of anxiety disorders (adjusted prevalence ratio = 1.45, 95 % confidence interval: 1.26, 1.67; P < 0.001) were also observed. When compared with nonbereaved parents, bereaved parents had significant increases in the risks of depression (P < 0.001), anxiety disorders (P < 0.001), marital break-up (P = 0.015), and physician visits for mental illness (P < 0.001) in the post-death period. In conclu-sion, parents who lose an offspring in a MVC experience considerable mental illness and marital disruption. anxiety; bereavement; depression; motor vehicle collision Abbreviations: ICD, International Classification of Disease; MVC, motor vehicle collision.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.001 | 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".