Research Division, Institute of Mental Health, Singapore, Singapore.
Nursing, Institute of Mental Health, Singapore, Singapore.
BMJ Open. 2019 Jul 11;9(7):e028179. doi: 10.1136/bmjopen-2018-028179.
(1) Investigate and explore whether different classes of associative stigma (the process by which a person experiences stigmatisation as a result of an association with another stigmatised person) could be identified using latent class analysis; (2) determine the sociodemographic and employment-related correlates of associative stigma and (3) examine the relationship between associative stigma and job satisfaction, among mental health professionals.
Cross-sectional online survey.
Doctors, nurses and allied health staff, working in Singapore.
Staff (n=462) completed an online survey, which comprised 11 associative stigma items and also captured sociodemographic and job satisfaction-related information. Latent class analysis was used to classify associative stigma on patterns of observed categorical variables. Multinomial logistic regression was used to examine associations between sociodemographic and employment-related factors and the different classes, while multiple linear regression analyses were used to examine the relationship between associative stigma and job satisfaction.
The latent class analysis revealed that items formed a three-class model where the classes were classified as 'no/low associative stigma', 'moderate associative stigma' and 'high associative stigma'. 48.7%, 40.5% and 10.8% of the population comprised no/low, moderate and high associative stigma classes, respectively. Multinomial logistic regression showed that years of service and occupation were significantly associated with moderate associative stigma, while factors associated with high associative stigma were education, ethnicity and occupation. Multiple linear regression analyses revealed that high associative stigma was significantly associated with lower job satisfaction scores.
Associative stigma was not uncommon among mental health professionals and was associated with sociodemographic factors and poorer job satisfaction. Associative stigma has received comparatively little attention from empirical researchers and continued efforts to address this understudied yet important construct in conjunction with future efforts to dispel misconceptions related to mental illnesses are needed.
(1) 通过潜在类别分析,探讨和研究是否可以识别不同类型的关联污名(即一个人由于与另一个受污名化的人有关联而经历污名化的过程);(2) 确定与关联污名相关的社会人口学和就业相关因素;(3) 考察精神卫生专业人员中关联污名与工作满意度之间的关系。
横断面在线调查。
在新加坡工作的医生、护士和相关卫生专业人员。
工作人员(n=462)完成了一项在线调查,其中包括 11 项关联污名项目,并收集了社会人口学和工作满意度相关信息。潜在类别分析用于根据观察到的分类变量模式对关联污名进行分类。多变量逻辑回归用于检验社会人口学和就业相关因素与不同类别之间的关联,而多元线性回归分析用于检验关联污名与工作满意度之间的关系。
潜在类别分析显示,项目形成了一个三类别模型,其中类别被分类为“无/低关联污名”、“中度关联污名”和“高关联污名”。分别有 48.7%、40.5%和 10.8%的人群属于无/低、中度和高度关联污名类别。多变量逻辑回归显示,服务年限和职业与中度关联污名显著相关,而与高度关联污名相关的因素是教育、族裔和职业。多元线性回归分析显示,高度关联污名与较低的工作满意度评分显著相关。
关联污名在精神卫生专业人员中并不罕见,与社会人口学因素和较差的工作满意度相关。关联污名受到实证研究人员的关注相对较少,需要继续努力解决这一研究不足但很重要的概念,同时需要与未来消除与精神疾病相关的误解的努力相结合。