Methods: A latent profile analysis (LPA) of 833 HPOG program participant survey data was conducted. LPA was used to divide participants into sub-groups according to their responses on the global PSS measure—a validated score created by calculating the difference between employment hope and perceived employment barriers—and by each sub-domain PSS measure. Mplus was used to conduct LPA by which program participants were classified based on their level of PSS. Among diverse model fit indices, Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), the Lo-Mendell-Rubin likelihood ratio test (LMR), and the bootstrapped likelihood ratio test (BLRT) were considered to best identify model fit. Propensity scores were calculated to control for other demographic covariates. A logistic and multiple regression analyses were conducted to examine the effects of PSS sub-group on employment status and ESS respectively.
Findings: With having the smallest AIC and BIC values, and statistically significant LMR and BLRT values, three sub-groups were found to conceptually have the best fit for PSS. Sub-group one (S1) has 11% of participants who are characterized by a high level of PSS. In contrast, sub-group two (S2) has 75% of participants who identify as possessing a low level of PSS. Sub-group three (S3) has 14% of participants who exhibit a moderate level of PSS. Results indicate that S1 were more likely than S2 to be employed and S1 had significantly greater ESS controlling for the propensity scores.
Implications: The findings from this study suggests that HPOG program participants as low-income jobseekers receiving healthcare training are not a homogenous group who possess the same level of PSS. Targeted social services can be provided through tailored interventions according to varying PSS levels and the ways in which PEB and EH intersect. A process-based strengthening and maintaining of PSS by reducing barriers and increasing and sustaining employment hope can help reach employment and ESS outcomes.