Differences in perceived fairness and health outcomes in two injury compensation systems: a comparative study
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».