Full Text

Turn on search term navigation

© 2019 Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ . Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Objectives

(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.

Design

Cross-sectional online survey.

Participants

Doctors, nurses and allied health staff, working in Singapore.

Methods

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.

Results

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.

Conclusion

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.

Details

Title
Associative stigma among mental health professionals in Singapore: a cross-sectional study
Author
Picco, Louisa 1 ; Chang, Sherilyn 1 ; Abdin, Edimansyah 1 ; Chua, Boon Yiang 1 ; Yuan, Qi 1 ; Janhavi Ajit Vaingankar 1 ; Ong, Samantha 2 ; Kah Lai Yow 3 ; Hong Choon Chua 4 ; Chong, Siow Ann 1 ; Subramaniam, Mythily 1 

 Research Division, Institute of Mental Health, Singapore, Singapore 
 Nursing, Institute of Mental Health, Singapore, Singapore 
 Allied Health, Institute of Mental Health, Singapore, Singapore 
 Chief Executive Office, Institute of Mental Health, Singapore, Singapore 
First page
e028179
Section
Mental health
Publication year
2019
Publication date
2019
Publisher
BMJ Publishing Group LTD
e-ISSN
20446055
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2289613207
Copyright
© 2019 Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ . Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.