Table 5.

Predictors of health post-injury

	Health post-injury
	Model 1				Model 2
Independent variable	OR	CI		p	AOR	CI		p
Fairness claims process	2.78	1.45,	5.33	.002	2.83	1.40,	5.71	.004
Age					1.01	0.99,	1.04	.23
Gender					0.91	0.45,	1.83	.78
Country of birth					1.00	0.48,	2.12	.99
Socio-economic status					1.12	0.57,	2.20	.74
Education					0.88	0.42,	1.88	.75
Marital status					0.90	0.46,	1.79	.77
Injury					1.35	0.57,	3.16	.49
Hospital admission					2.04	0.97,	4.26	.06
Time after injury					0.62	0.32,	1.20	.16
Health pre-injury					6.15	1.09,	34.61	.04

Note: Model 1 Nagelkerke R² = 0.08; Model 2 Nagelkerke R² = 0.16

Multiple logistic regression analysis, modelling the probability of good or excellent health (versus fair or poor health). Model 1 explores the unadjusted association between the overall fairness perception and health. Model 2 adjusts for demographic, injury variables, and pre-injury health. There was no multicollinearity

Coding: Overall fairness claims process (0 = disagree/neutral; 1 = agree); Gender (0 = Male; 1 = Female); Country of birth (0 = Other; 1 = Australia); Socio-economic status (0 = Lower; 1 = Higher); Education (0 = Low/Medium; 1 = High); Marital status (0 = Single/Divorced; 1 = Married); Injury (0 = Other; 1 = Whiplash/soft tissue injury); Hospital admission (0 = No; 1 = Yes); Time after injury (0 = 12 months; 1 = 24 months); Health pre-injury (0 = Poor; 1 = Good); Health post-injury (0 = poor/fair, 1 = good/excellent). Reference category = 0