Differences in perceived fairness and health outcomes in two injury compensation systems: a comparative study
Bibliographic record
Abstract
BACKGROUND: Involvement in a compensation process following a motor vehicle collision is consistently associated with worse health status but the reasons underlying this are unclear. Some compensation systems are hypothesised to be more stressful than others. In particular, fault-based compensation systems are considered to be more adversarial than no-fault systems and associated with poorer recovery. This study compares the perceived fairness and recovery of claimants in the fault-based compensation system in New South Wales (NSW) to the no-fault system in Victoria, Australia. METHODS: One hundred eighty two participants were recruited via claims databases of the compensation system regulators in Victoria and NSW. Participants were > 18 years old and involved in a transport injury compensation process. The crash occurred 12 months (n = 95) or 24 months ago (n = 87). Perceived fairness about the compensation process was measured by items derived from a validated organisational justice questionnaire. Health outcome was measured by the initial question of the Short Form Health Survey. RESULTS: In Victoria, 84 % of the participants considered the claims process fair, compared to 46 % of NSW participants (χ(2) = 28.54; p < .001). Lawyer involvement and medical assessments were significantly associated with poorer perceived fairness. Overall perceived fairness was positively associated with health outcome after adjusting for demographic and injury variables (Adjusted Odds Ratio = 2.8, 95 % CI = 1.4 - 5.7, p = .004). CONCLUSION: The study shows large differences in perceived fairness between two different compensation systems and an association between fairness and health. These findings are politically important because compensation processes are designed to improve recovery. Lower perceived fairness in NSW may have been caused by potential adversarial aspects of the scheme, such as liability assessment, medical assessments, dealing with a third party for-profit insurance agency, or financial insecurity due to lump sum payments at settlement. This study should encourage an evidence informed discussion about how to reduce anti-therapeutic aspects in the compensation process in order to improve the injured person's health.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".