Digital Methods for the Spiritual and Mental Health of Generation Z: Scoping Review (Preprint)
Bibliographic record
Abstract
BACKGROUND Generation Z (Gen Z) includes individuals born between 1995 and 2012. These individuals experience high rates of anxiety and depression. Most Gen Z individuals identify with being spiritual, and aspects from religion and spirituality can be integrated into mental health treatment and care as both are related to lower levels of depression. However, research on the spiritual and mental health of Gen Z is sparse. To date, there are no systematic or scoping reviews on digital methods to address the spiritual and mental health of Gen Z. OBJECTIVE This scoping review aimed to describe the current state of digital methods to address spiritual and mental health among Gen Z, identify the knowledge gaps, and make suggestions for how to leverage digital spiritual and mental health interventions for Gen Z. METHODS A comprehensive literature search was conducted in PubMed, Scopus, PsycInfo, CINAHL, Education Full Text, Google Scholar, SocIndex, and Sociological Abstracts. The inclusion criteria were as follows: (1) study population born between 1995 and 2012 (ie, Gen Z); (2) reporting on spiritual health or well-being, spirituality or religion, and mental health or well-being; (3) reporting on using digital methods; (4) publication in 1996 or beyond; (5) human subject research; (6) full text availability in English; (7) primary research study design; and (8) peer-reviewed article. Two authors screened articles and subsequently extracted data from the included articles to describe the available evidence. RESULTS A total of 413 articles were screened at the title and abstract levels, of which 27 were further assessed with full text for eligibility. Five studies met the inclusion criteria, and data were extracted to summarize study characteristics and findings. The studies were performed across 4 different countries. There were 2 mixed-methods studies (South Africa and Canada), 2 cross-sectional studies (China and United States), and 1 randomized controlled trial (United States). Of these studies, only 2 discussed digital interventions (a text messaging–based intervention to improve spiritual and mental health, and a feasibility study for a mental health app). Other studies had a digital component with minor or unclear spiritual and mental health measures. Overall, there was a lack of consistency in how spiritual and mental health were measured. CONCLUSIONS Few studies have focused on assessing the spiritual and mental health of Gen Z in the digital context, and no research to date has examined a digital spiritual and mental health application among Gen Z. Research is needed to inform the development and evaluation of approaches to address the spiritual and mental health of Gen Z via digital means (eg, mobile apps).
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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 | 0.019 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".