Mood Lifters: A Dimensional Approach to Mental Health Care

Pokowitz, E. L.; Prakash, N.; Foster, K. T.; Deldin, P. J.

doi:10.1007/s41811-024-00204-5

Cited by 2 publications

(2 citation statements)

References 62 publications

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“…Due to the presence of distinct themes within this analysis, it is additionally possible that ML works differently for different individuals; while social support and connection may be the driver for some, more direct skill learning may be the driver for others. In this sense, ML may provide many different paths toward mental wellness, as theorized in Pokowitz et al, 2024 [ 5 ].…”

Section: Discussionmentioning

confidence: 99%

“…For those experiencing more severe symptom levels, ML may be adjunctive to other individual or group-based mental health care (e.g., [4]). For detailed information on the ML program and content, see Pokowitz et al, 2024 [5]. The efficacy and effectiveness of ML have previously been established across various populations, including the general adult population [6][7][8], seniors [9], individuals with bipolar disorder [4], parents of children with complex medical needs [10], and student athletes [11].…”

Section: Introductionmentioning

confidence: 99%

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Mood Lifters for Graduate Students and Young Adults: A Mixed-Methods Investigation into Mechanisms of Change in Online Group Therapy

Pokowitz,

Prakash,

Planaj

et al. 2024

Behavioral Sciences

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Mood Lifters (ML) is a dimensional, group-based, peer-led mental health program that has shown efficacy in mitigating psychopathology and promoting wellness within a variety of populations. There is not yet evidence for mechanism(s) driving these changes. Qualitative data exploring the lived experiences of participants may be a unique way to develop hypotheses about the potential mechanisms driving change. The current study used qualitative and quantitative data from the ML for Graduate Students (ML-GS) and Young Adults (ML-YA) studies to generate hypotheses regarding potential mechanisms of changes experienced in ML. Seventy-nine graduate students and fifty-nine non-student young adults provided quantitative and qualitative feedback after participating in a 12-week virtual ML program. Inductive qualitative analysis was conducted within a reflexive thematic framework. Descriptive statistics of quantitative measures were also calculated. Three themes suggest potential directions for future mechanism research on virtual ML programs. Most participants emphasized the social connections built in groups as the most potent and powerful aspects of ML, while others focused on the design or content of the program. Quantitative data presented contextualize the lived experiences of participants. Future ML research should explore the three themes identified within this study through continued qualitative and quantitative data collection and analysis. NCT05078450.

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Section: Discussionmentioning

confidence: 99%