Charlotte Price Disclosure Data
Description
Dataset for Charlotte Price Disclosure Study: Abstract: An important method of treatment that can be utilized to address the New Zealand Mental Health crisis is vocational and occupational rehabilitation in one’s workplace. For individuals to access this treatment, they first must disclose an illness to their employer. Due to the engrained societal stigma towards mental illness, New Zealanders often fear the process of disclosure. For an already vulnerable population, supporting the disclosure of mental illness may be a critical process to retain and accommodate these employees. The aim of this study is to examine how perceived organisational and supervisory support influence an individual’s willingness to disclose a mental illness diagnosis to their employer, to determine if this general willingness leads to an act of disclosure, and to understand the impact of disclosure willingness and disclosure behaviour on individual workplace well-being. In a correlational cross-sectional survey using measures of perceived organisational and supervisory support, individual well-being, disclosure willingness and behaviour, 508 responses were obtained and analysed. A conceptual framework was supported by significant results that showed perceived organisational and supervisory support influence willingness to disclose mental illness and individual workplace well-being. Willingness to disclose mental illness was a mediating variable in the relationship between perceived support and well-being. Willingness to disclose mental illness predicted disclosure behaviour, but disclosure behaviour did not have an impact on individual workplace well-being. These results have numerous theoretical and practical applications, and provide a critical understanding for creating a culture of acceptance in New Zealand workplaces.
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Steps to reproduce
Prior to hypothesis testing, a correlation matrix was produced using all relevant variables (table 4). Data analysis was conducted using IBM SPSS Statistics and Jamovi, several preparatory data analysis steps were conducted. The dataset was filtered for bots and incomplete responses were removed, reducing the dataset to 380 responses. The filtering was followed by factor and reliability analyses, and then variable computation was completed.