Posttraumatic Stress Disorder, Depression, and Prolonged Grief Disorder in Families Bereaved by a Traumatic Workplace Death: The Need for Satisfactory Information and Support
Bibliographic record
Abstract
The impact of traumatic workplace death on bereaved families, including their mental health and well-being, has rarely been systematically examined. This study aimed to document the rates and key correlates of probable posttraumatic stress disorder (PTSD), major depressive disorder (MDD), and prolonged grief disorder (PGD) in family members following a workplace injury fatality. The hidden nature of the target population necessitated outreach recruitment techniques, including the use of social media, newspaper articles, radio interviews, and contact with major family support organizations. Data were collected using a cross-sectional design and international online survey. The PCL-C (PTSD), the PHQ-8 (MDD), and PG-13 (PGD) were used to measure mental health disorders. All are well-established self-report measures with strong psychometric qualities. Participants were from Australia (62%), Canada (17%), the USA (16%), and the UK (5%). The majority were females (89.9%), reflecting the gender distribution of traumatic workplace deaths (over 90% of fatalities are male). Most were partners/spouses (38.5%) or parents (35%) and over half (64%) were next of kin to the deceased worker. Most deaths occurred in the industries that regularly account for more than 70 percent of all industrial deaths-construction, manufacturing, transport, and agriculture forestry and fishing. At a mean of 6.40 years (SD = 5.78) post-death, 61 percent of participants had probable PTSD, 44 percent had probable MDD, and 43 percent had probable PGD. Logistic regressions indicated that a longer time since the death reduced the risk of having each disorder. Being next of kin and having a self-reported mental health history increased the risk of having MDD. Of the related information and support variables, having satisfactory support from family, support from a person to help navigate the post-death formalities, and satisfactory information about the death were associated with a decreased risk of probable PTSD, MDD, and PGD, respectively. The findings highlight the potential magnitude of the problem and the need for satisfactory information and support for bereaved families.
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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 | 0.007 |
| 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) | 0.001 | 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".