Prevalence of second victims, risk factors and support strategies among young German physicians in internal medicine (SeViD-I survey)

Table 4 Factors influencing the symptom load of second victims

		High symptom load of second victims (n = 314)
Independent variable		Final model r² = 0.06^a
Independent variable		ReCoB^b	p	odds ratio^c	95%-CI^d
Gender^e (female)		0.69	0.01	1.99	1.18–3.36
Age (years)	25–30
	31–32	− 0.22	0.50	0.80	0.42–1.51
	33–36	−0.12	0.72	0.89	0.47–1.70
Years in training	1–3
	4–5	−0.31	0.32	0.74	0.40–1.35
	6–13	−0.58	0.18	0.56	0.24–1.32
Specialty status^f (specialist)		0.69	0.11	2.00	0.85–4.70
Workplace in acute care^g		−0.40	0.11	0.67	0.41–1.01

For the construction of the symptom load score, see the Methods section. For this binary logistic regression model, the symptom score was split based on its median in two groups with lower (0 to 8.5 points) vs. higher (9 to 20 points) symptom load scores
^a, Nagelkerkes r²; ^b, regression coefficient B; ^c, exponentiation of the B coefficient (Exp(B)) or odds ratio; ^d, confidence interval; ^e, reference category is male sex; ^f, reference category is no medical specialty; ^g, reference category is not working in acute care (predominantly in ICU and/or emergency department)

ISSN: 1745-6673