Exploring Generation Z and Young Millennials’ Perspectives of a Spiritual Self-Care App and Their Spiritual Identity (Skylight): Qualitative Semistructured Interview Study
Bibliographic record
Abstract
BACKGROUND: Generation Z and young millennials (ages 18-35 years), collectively referred to as GenZennials, are connected to technology and the internet like no other generation before them. This has mental health implications, such as increased rates of anxiety and stress. Recent research has shown that app-based mental health interventions can be useful to address such mental health concerns. However, spirituality is an untapped resource, especially since GenZennials largely identify as spiritual and already integrate spiritual practices into their self-care. OBJECTIVE: There were four objectives to this study: (1) comprehensively explore reasons why GenZennials use a spiritual self-care app (ie, Skylight; Radiant Foundation), (2) understand how GenZennials identify spiritually, (3) understand the app's relevance to GenZennials, and (4) gather feedback and suggestions to improve the app. METHODS: Semistructured interviews were conducted with 23 GenZennials (ages 18-35 years; mean 28.7, SD 5.0 years; n=20, 87% female) who used the Skylight app. Interviews were 30 to 60 minutes and conducted on Zoom. Thematic analysis was used to analyze interviews. RESULTS: Five major categories emerged from the analysis, each encompassing one to several themes: (1) reasons for using the Skylight app, (2) content favorites, (3) defining spiritual identity, (4) relevance to GenZennials, and (5) overall improvement recommendations. Participants used the app for various reasons including to relax, escape, or ground themselves; improve mood; and enhance overall health and wellness. Participants also cited the app's variety of content offerings and its free accessibility as their primary reasons for using it. Most participants identified themselves as solely spiritual (8/23/35%) among the options provided (ie, spiritual or religious or both), and they appreciated the app's inclusive content. Participants felt that the app was relevant to their generation as it offered modern content (eg, spiritual self-care activities and short content). Participants recommended adding more personalization capabilities, content, and representation to the app. CONCLUSIONS: This is the first study to qualitatively explore GenZennials' perspectives and the use of a spiritual self-care app. Our findings should inform the future creation and improvement of spiritual self-care apps aimed at cultivating GenZennials' spiritual and mental well-being. Future research is warranted to examine the effects of using a spiritual self-care app on GenZennial mental health.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".