Table 2.

Comparison of PCC and SDM between patient groups

	PCC	SDM
Gender
- Man	2.1 (0.63)	2.4 (0.87)
- Women	2.1 (0.71)	2.3 (0.83)
	(p > 0.60)	(p > 0.10)
	t = 0.447; n = 574	t = 1.425; n = 573
Education
- Low	2.2 (0.65)	2.3 (0.78)
- Medium	2.1 (0.66)	2.4 (0.77)
- High	2.0 (0.56)	2.5(0.81)
	(p > 0.40)	(p > 0.20)
	t = 0.741; n = 366	t = 1.250; n = 366
Age
- 18–65 years	2.1 (0.69)	2.3 (0.83)
- 65+70 years	2.0 (0.65)	2.2 (0.93)
	(p > 0.70)	(p > 0.40)
	t = 0.243; n = 516	t = 0.706; n = 516
Functional health status
- Poor/average	2.2 (0.62)	2.3 (0.75)
- Good/very good/excellent	2.2 (0.65)	2.4 (0.84)
	(p > 0.60)	(p > 0.30)
	t = 0.443; n = 412	t = 0.889; n = 411
Chronic conditions
- No	2.1 (0.65)	2.3 (0.83)
- Yes	2.1 (0.64)	2.4 (0.80)
	(p > 0.60)	(p > 0.05)
	F = 0.397; n = 426	t = 1.653; n = 427

Legend: Mean scores and standard deviations between brackets. To correct for clustering of patients within GPs the original t-values have been divided by the square root of the 'design effect', which was 2.01 for PCC (ICC = 0.34) and 1.31 for SDM (ICC = 0.19). The design effect is 1+(n-1)*ICC, where n is the average cluster size (n = 10 in our study) and ICC the intracluster correlation.