Hidden costs, hidden lives: Financial effects of fatal work injuries on families
Bibliographic record
Abstract
Abstract Although workplace death is known to have profound social and psychological effects on families, the economic consequences have not been explored. This pioneering study investigated families’ financial situations following fatal workplace injuries. An online survey explored the impact of post-death financial change on 142 participants from Australia, Canada, the USA, and the UK using a scale from the economic strain model. Half of the participants experienced financial loss, and the proportion struggling financially increased from 24% to 62% after the death. Workers’ compensation claims were made by 74% of participants, but they reported problems with delays, levels of entitlement, and satisfaction with the scheme. Other key sources of assistance were family and friends or support groups and services. Participants who were older, next-of-kin, and partner/spouses were significantly more likely to experience financial loss as were those whose deceased relative worked 51+ hours per week, possibly because the deceased was self-employed or worked significant overtime not covered by compensation settlements. Those experiencing financial loss sought short- and long-term financial help, accessed social security, re-entered the workforce, acquired mental disorders, and experienced declines in physical health, at significantly higher rates than participants without financial loss, and their children developed mental health problems significantly more often. Findings highlight the detrimental, and potentially intergenerational, effects of financial loss on the health and wellbeing of families bereaved by traumatic workplace deaths. Policy issues flowing from the results are discussed, including how this informs wider debates on refashioning regulatory protection.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".