A Concise Occupational Mental Health Screening Tool for South African Workplaces.

Mental health in the workplace is becoming of ever greater importance. General occupational health surveillance programmes are already in widespread use, with established referral systems for treatment and rehabilitation, and the same mechanisms could be expanded to include mental health screening and intervention. This study aimed to develop a concise composite mental health screening tool, based on analysis of existing data, for application in routine occupational health surveillance in South Africa. Data from workplace occupational health surveillance programs from 2,303 participants were analysed. Participants completed a number of questions/scaled items collated into a survey format, and partook in an interview with a psychologist. The data was analysed using frequency of positive self-reports, Chi square to calculate associations with outcomes, Receiver Operator Characteristic curve analysis to explore predictive ability, and binomial logistic regression to calculate the relative contribution of markers to outcomes. An exploratory factor analysis was further conducted on identified items. A general workplace model with 14 markers (and a maritime workplace model with 17 markers) were identified. The factor analysis suggested their organisation into five domains (similar for both models), namely neurocognitive health, common mental disorders, history of adaptation in occupational specific contexts, family-work interface, and stress overload. The study's data-driven approach proposed a concise composite screener with less than 50 items, comprising five domains. This tool appears useful in identifying employees at risk for workplace injuries or poor mental health outcomes, and could be applied to related workplace settings in South Africa.
Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
(Copyright © 2022 Van Wijk, Martin and Meintjes.)