Virtual mindfulness interventions to promote well-being in young adults: A mixed-methods systematic review
Bibliographic record
Abstract
ABSTRACT Background With the onset of the COVID-19 pandemic, students have experienced drastic changes in their academic and social lives with ensuing consequences towards their physical and mental well-being. The purpose of this systematic review is to identify virtual mindfulness-based interventions for the well-being of young adults aged 15 to 40 years in developed countries and examine the efficacy of these techniques/exercises. Methods This mixed-methods systematic review follows the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines with a registered PROSPERO protocol. With a convergent integrated synthesis approach, IEEE Xplore, PsychInfo, Web of Science and OVID were searched with a predetermined criteria and search strategy employing booleans and filters for peer-reviewed and grey literature. Data screening and extraction were independently performed by two authors, with a third author settling disagreements after reconciliation. Study quality of selected articles was assessed with two independent authors using the Mixed Methods Appraisal Tool (MMAT). Studies were analyzed qualitatively (precluding meta and statistical analysis) due to the heterogeneous study results from diverse study designs in present literature. Results Common mindfulness-based interventions used in the appraised studies included practicing basic mindfulness, Mindfulness-Based Stress Reduction (MBSR) programs, Mindfulness-Based Cognitive Therapy programs (MBCT) and the Learning 2 BREATHE (L2B) program. Conclusion Studies implementing mindfulness interventions demonstrated an overall improvement in well-being. Modified versions of these interventions can be implemented in a virtual context, so young adults can improve their well-being through an accessible format.
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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 | 0.018 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".