Methods: We conducted confirmatory factor analysis using secondary data (combined samples of 495 DSPs) to examine whether the original three-factor structure (i.e., CS, BO, and STS) holds for DSPs. Our sample included more females (80%), 20-39 years old (58%), with bachelor’s degree or lower (90%), and having 5 or more years of experience in the field (52%). We evaluated the best fit model using modification indices which suggest the potential improvement of our model specification based on significant reduction of chi-square values. Further, we tested if there were any significant differences in demographics on ProQOL scores.
Results: Modification indices suggested the removal of four items (i.e., “belief,” “caring,” “preoccupied,” and “startled”). Further, “losing sleep” was found to be better aligned with STS than BO. In addition, “trapped” (originally under BO) was associated with both BO and STS, and “on edge” (originally under STS) was associated with both STS and BO. The three-factor model with these modifications revealed acceptable fit (Comparative Fit Index = 0.93, Tucker-Lewis Index = 0.92, Standardized Root Mean Residual = 0.06, Root Mean Square Error of Approximation = 0.06). CS (α=.92) was negatively correlated with BO (α=.82) [r=- 0.8] and STS (α=.85) [r=-0.4], while BO and STS were positively correlated (r=0.4). Females had significantly higher CS, lower BO, and lower STS (p<.01) than males. Age, educational degree, and length of time in the field were not significantly related to ProQOL.
Conclusions and Implications: The three-factor model (Stamm, 2010) demonstrated an acceptable fit using the relatively large data, and the utility of ProQOL; yet some model refinements were suggested for DSPs. In addition, relationships between gender, length of time in the field, and the ProQOL scores contrasted with Stamm’s findings. Similar discrepancies have been noted across different populations (Galiana et al., 2017; Hemsworth et al., 2018; Samson et al., 2016), which warrant further investigation of ProQOL for the DSP population. The social work practice and research implications of these findings will be discussed for addressing the unique job characteristics and wellbeing factors among DSPs.