小学教师职业倦怠维度发展顺序探究——来自结构方程模型和交叉滞后网络分析模型的证据

doi:10.3724/SP.J.1041.2022.00371

摘要/Abstract

摘要：

教师职业倦怠的情感衰竭、去人性化和个人成就感降低3个维度相对独立但也存在相互影响, 对其发展关系进行研究, 有助于深入理解职业倦怠的发展过程, 尽早识别倦怠症状。本研究对3837名小学教师进行追踪测试, 测试间隔为3年, 采用结构方程模型和交叉滞后网络分析模型进行分析, 结果发现小学教师职业倦怠维度最优发展模型为“T1的情感衰竭和个人成就感降低分别预测T2的情感衰竭和个人成就感降低, T1的去人性化预测T2的去人性化和个人成就感降低”, 且最优发展模型具有性别间的一致性和教龄段之间的一致性。这一结果支持并强调了去人性化在小学教师职业倦怠发展中的重要作用, 对识别教师职业倦怠早期症状并采取相应措施有效阻断教师职业倦怠的进一步发展具有一定的理论和现实意义。

关键词: 小学教师, 职业倦怠, 结构方程模型, 交叉滞后网络分析模型, 发展模型

Abstract:

The three dimensions of teacher’s job burnout, emotional exhaustion, depersonalization and reduced personal accomplishment, are relatively independent but also have mutual influence. Research into their developmental relationship is helpful to understand the developmental process of job burnout and identify the early symptoms of job burnout. 3837 primary school teachers took part in this two-wave longitudinal study with interval for three years. We conducted structural equation model (SEM) to compare five representative developmental models, basic model and full model, while using cross-lagged panel network model (CLPN) to highlight pathways among three dimensions and to reveal pathways among the constituting variables within each dimension. In the cross-lagged panel network model, the relations among individual items were modeled both within and across time point.
Results of SEM showed that when considering the effect size r > 0.1, the optimal development model for primary school teachers’ job burnout dimensions was “T1 emotional exhaustion and reduced personal accomplishment separately predicted T2 emotional exhaustion and reduced personal accomplishment, T1 depersonalization predicted T2 depersonalization and T2 reduced personal accomplishment”.
Results of CLPN showed that the center of the network was an important outcome “experiencing positive impact and value at work” (item 3 of reduced personal accomplishment) and an important predictor “not caring what students think” (item 4 of depersonalization). The strongest pathways in the network were the effect of “experiencing positive impact and value at work” (item 3 of reduced personal accomplishment) on “not caring what students think” (item 4 of depersonalization) and the effect of “insomnia and headache caused by work” (item 8 of emotional exhaustion) on “exhaustion and depression” (item 2 of emotional exhaustion). While the former belonged to the vertical process between depersonalization and reduced personal accomplishment, the latter belonged to the vertical process within emotional exhaustion. The direct impacts of emotional exhaustion on depersonalization and reduced personal accomplishment on emotional exhaustion existed but the strengths were obvious weaker than the pathways above. The results supported the optimal development model.
Both SEM and CLPN results indicate that depersonalization plays an important role in teacher burnout. One suggestion is to include the evaluations of teachers’ relationships with students, colleagues and leaders to identify the depersonalization symptoms in time, which may effectively prevent the further development of teacher burnout.

Key words: primary school teachers, job burnout, structural equation model, cross-lagged panel network model, development model

中图分类号:

B849: G44

谢敏, 李峰, 罗玉晗, 柯李, 王侠, 王耘. (2022). 小学教师职业倦怠维度发展顺序探究——来自结构方程模型和交叉滞后网络分析模型的证据. 心理学报, 54(4), 371-384.

XIE Min, LI Feng, LUO Yuhan, KE Li, WANG Xia, WANG Yun. (2022). A developmental model of job burnout dimensions among primary school teachers: Evidence from structural equation model and cross-lagged panel network model. Acta Psychologica Sinica, 54(4), 371-384.

图/表 12

参考文献 45

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维度	M	SD	2	3	4	5	6
1 情感衰竭T1	2.67	0.98	0.62^**	0.04^*	0.47^**	0.29^**	0.09^**
2 去人性化T1	1.79	0.66		0.16^**	0.31^**	0.40^**	0.20^**
3 成就感降低T1	2.50	0.99			0.04^*	0.12^**	0.25^**
4 情感衰竭T2	2.47	0.93				0.67^**	0.14^**
5 去人性化T2	1.78	0.72					0.31^**
6 成就感降低T2	2.22	0.84

维度	M	SD	2	3	4	5	6
1 情感衰竭T1	2.67	0.98	0.62^**	0.04^*	0.47^**	0.29^**	0.09^**
2 去人性化T1	1.79	0.66		0.16^**	0.31^**	0.40^**	0.20^**
3 成就感降低T1	2.50	0.99			0.04^*	0.12^**	0.25^**
4 情感衰竭T2	2.47	0.93				0.67^**	0.14^**
5 去人性化T2	1.78	0.72					0.31^**
6 成就感降低T2	2.22	0.84

模型	χ²	df	RMSEA	SRMR	CFI	TLI	AIC	aBIC
基础模型	160.63	6	0.08	0.05	0.96	0.93	24076.50	24113.40
Golembiewski (1983)模型	49.45	4	0.05	0.03	0.99	0.97	23969.32	24012.36
Leiter & Maslach (1988)模型	25.35	4	0.04	0.02	0.99	0.98	23945.22	23988.27
Lee & Ashforth (1993)模型	99.28	4	0.08	0.03	0.98	0.93	24019.15	24062.19
van Dierendonck (2001)模型	145.37	4	0.09	0.05	0.97	0.90	24065.24	24108.29
Taris等(2005)模型	22.97	3	0.04	0.02	0.99	0.98	23944.83	23990.96
调整模型1	0.61	2	0.00	0.00	1	1	23924.48	23973.68
调整模型2	51.24	5	0.05	0.03	0.99	0.97	23969.11	24009.08

模型	χ²	df	RMSEA	SRMR	CFI	TLI	AIC	aBIC
基础模型	160.63	6	0.08	0.05	0.96	0.93	24076.50	24113.40
Golembiewski (1983)模型	49.45	4	0.05	0.03	0.99	0.97	23969.32	24012.36
Leiter & Maslach (1988)模型	25.35	4	0.04	0.02	0.99	0.98	23945.22	23988.27
Lee & Ashforth (1993)模型	99.28	4	0.08	0.03	0.98	0.93	24019.15	24062.19
van Dierendonck (2001)模型	145.37	4	0.09	0.05	0.97	0.90	24065.24	24108.29
Taris等(2005)模型	22.97	3	0.04	0.02	0.99	0.98	23944.83	23990.96
调整模型1	0.61	2	0.00	0.00	1	1	23924.48	23973.68
调整模型2	51.24	5	0.05	0.03	0.99	0.97	23969.11	24009.08

模型	路径	b	SE	t	β	r
Golembiewski (1983)模型	ee1→ee2	0.40	0.01	36.82	0.44	0.49
	pa1→ee2	0.01	0.01	1.40	0.01	0.06
	dp1→dp2	0.41	0.01	29.00	0.38	0.43
	dp1→pa2	0.21	0.02	10.45	0.16	0.21
	pa1→pa2	0.18	0.01	13.77	0.21	0.26
Leiter & Maslach (1988)模型	ee1→ee2	0.44	0.01	34.00	0.44	0.49
	ee1→dp2	0.06	0.01	5.17	0.06	0.11
	dp1→dp2	0.36	0.0	24.33	0.36	0.41
	dp1→pa2	0.21	0.02	10.65	0.21	0.26
	pa1→pa2	0.18	0.01	13.61	0.18	0.23
Taris等 (2005)模型	ee1→ee2	0.42	0.02	25.00	0.45	0.50
	dp1→ee2	0.04	0.03	1.50	0.03	0.08
	ee1→dp2	0.05	0.01	4.15	0.07	0.12
	dp1→dp2	0.38	0.02	19.25	0.35	0.40
	dp1→pa2	0.21	0.02	10.75	0.17	0.22
	pa1→pa2	0.18	0.01	13.69	0.21	0.26
调整模型1	ee1→ee2	0.44	0.01	34.08	0.47	0.52
	pa1→ee2	0.05	0.01	3.92	0.05	0.10
	ee1→dp2	0.07	0.01	5.75	0.09	0.14
	dp1→dp2	0.35	0.02	22.06	0.33	0.38
	pa1→dp2	0.05	0.01	4.82	0.07	0.12
	dp1→pa2	0.21	0.02	10.55	0.16	0.21
	pa1→pa2	0.19	0.01	14.77	0.22	0.28
调整模型2	ee1→ee2	0.40	0.01	36.73	0.43	0.48
	dp1→dp2	0.40	0.01	31.08	0.38	0.43
	dp1→pa2	0.21	0.02	10.45	0.16	0.21
	pa1→pa2	0.18	0.01	13.85	0.21	0.26