Olga Tennison Autism Research Centre, La Trobe University, Melbourne, Australia.
La Trobe University, Melbourne, Australia.
Autism Res. 2024 Jul;17(7):1417-1449. doi: 10.1002/aur.3129. Epub 2024 Apr 25.
Researchers have begun to explore the characteristics and risk factors for autistic burnout, but assessment tools are lacking. Our study comprehensively examined and compared the psychometric properties of the unpublished 27-item AASPIRE Autistic Burnout Measure (ABM), and personal and work scales of the Copenhagen Burnout Inventory (CBI) to evaluate their efficacy as screening measures for autistic burnout, with a group of 238 autistic adults. Exploratory factor analyses (EFA) revealed a 4-factor structure for the ABM and a 2-factor structure for the CBI personal scale (CBI-P). Factorial validity and dimensionality were examined with four exploratory models which indicated a unidimensional structure for the ABM with an overarching 'Autistic Burnout' construct, and multidimensional CBI-P structure comprising two subscales and overarching 'Personal Burnout' construct. Other reliability and validity indicators included Spearman correlations, analysis of variance, receiver operating characteristics, sensitivity, specificity, and intra-class correlations (ICC). The ABM and CBI-P were strongly correlated with depression, anxiety, stress, and fatigue. Unexpectedly, correlations between the burnout measures and camouflaging, and wellbeing measures were moderate. Potential overlap between burnout and depression and fatigue was examined through EFA, which supported convergent validity of the ABM and depression measure, while correlations and ICC analyses revealed mixed results. We concluded that the ABM and the CBI-P Emotional Exhaustion subscale were valid preliminary screening tools for autistic burnout. Testing with larger and more diverse autistic samples is required to further examine the psychometric properties of the ABM, and to understand the relationships between autistic burnout and depression, and masking.
研究人员已经开始探索自闭症倦怠的特征和风险因素，但评估工具仍然缺乏。我们的研究全面考察和比较了未发表的 27 项 AASPIRE 自闭症倦怠量表（ABM）以及哥本哈根倦怠量表（CBI）的个人和工作量表的心理测量特性，以评估它们作为自闭症倦怠筛查工具的有效性，研究对象为 238 名自闭症成年人。探索性因素分析（EFA）显示，ABM 具有 4 因素结构，CBI 个人量表（CBI-P）具有 2 因素结构。通过四个探索性模型检验了因子有效性和维度，表明 ABM 具有单一维度结构，涵盖了“自闭症倦怠”的总体结构，而 CBI-P 结构则包含两个子量表和涵盖了“个人倦怠”的总体结构。其他可靠性和有效性指标包括斯皮尔曼相关系数、方差分析、接收者操作特征、灵敏度、特异性和组内相关系数（ICC）。ABM 和 CBI-P 与抑郁、焦虑、压力和疲劳密切相关。出乎意料的是，倦怠测量值与伪装和幸福感测量值之间的相关性为中度。通过 EFA 研究了倦怠测量值与抑郁和疲劳之间的潜在重叠，这支持了 ABM 和抑郁测量值的收敛有效性，而相关性和 ICC 分析则得出了混合的结果。我们的结论是，ABM 和 CBI-P 的情绪耗竭分量表是自闭症倦怠的有效初步筛查工具。需要对更大和更多样化的自闭症样本进行测试，以进一步检验 ABM 的心理测量特性，并了解自闭症倦怠与抑郁和伪装之间的关系。
