Aims This study assessed the feasibility of delivering three good things (3GTs) practice as part of professional nurse residency program, measured the degree to which it influenced work–life balance, resilience, and burnout, and explored what newly licensed nurses (NLRNs) identified as good things. Background Burnout occurs in response to chronic work‐related emotional and interpersonal stress, negatively impacting nurses and patients. However, research shows that 3GT practice can increase positive emotions, enhance resilience, and reduce burnout. Methods In this study, 3GT was introduced to a convenience sample of 115 NLRNs during their professional residency program. For 14 days, participants received daily 3GT prompts. Individualized survey links were sent via SMS message at baseline, postsurvey (T1), and 6 months (T2). Survey data were collected about work–life balance, burnout, and resilience, and text data from participants' daily 3GT notations from March through November 2021. Results Seventy‐one participants were recruited. T1 survey results indicated significant improvements in survey measures but only emotional recovery improvement was sustained at T2. Burnout was the only variable that correlated to participants' number of 3GT days practice. Simple joys, reflections about work, self‐care activities, and relationships were major identified themes. Conclusions The results demonstrate the generalizability, value, and feasibility of implementing a web‐based 3GT intervention in a nurse residency program. Additional benefits may be those gained by the reflection that is prompted, thereby facilitating professional development among NLRNs.

We propose a generalized linear low‐rank mixed model (GLLRM) for the analysis of both high‐dimensional and sparse responses and covariates where the responses may be binary, counts, or continuous. This development is motivated by the problem of identifying vaccine‐adverse event associations in post‐market drug safety databases, where an adverse event is any untoward medical occurrence or health problem that occurs during or following vaccination. The GLLRM is a generalization of a generalized linear mixed model in that it integrates a factor analysis model to describe the dependence among responses and a low‐rank matrix to approximate the high‐dimensional regression coefficient matrix. A sampling procedure combining the Gibbs sampler and Metropolis and Gamerman algorithms is employed to obtain posterior estimates of the regression coefficients and other model parameters. Testing of response‐covariate pair associations is based on the posterior distribution of the corresponding regression coefficients. Monte Carlo simulation studies are conducted to examine the finite‐sample performance of the proposed procedures on binary and count outcomes. We further illustrate the GLLRM via a real data example based on the Vaccine Adverse Event Reporting System.

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