Self-Compassionate Engagement and Cultural Background Explain Gains in Emerging Adults’ Temporal Well-Being After a Virtual Reality Experience
Bibliographic record
Abstract
An established empirical connection links adverse childhood experiences (ACEs) with the development of self-critical behaviours, including habitual self-scrutiny and harsh self-evaluation. These tendencies inhibit self-compassion and reduce well-being, particularly among emerging adults (ages 16–30) for whom such adversity may be recent or ongoing. We conducted a pilot study examining (a) the relationship between baseline compassionate abilities and multidimensional temporal well-being, and (b) the potential impact of a self-transcendent virtual reality (VR) meditation experience, Awedyssey, on well-being. Participants (N = 8) completed pre-study, pre-VR, and post-VR assessments of compassionate abilities, well-being, flow state, and anxiety, as well as semi-structured interviews. Results indicated that self-compassionate action and engagement could predict improvements across several domains and temporal dimensions of well-being. However, contrary to expectations, immersive flow and self-transcendent emotions elicited by Awedyssey did not produce measurable changes in anxiety or well-being. These findings support a robust connection between self-compassion and well-being, while highlighting limitations in the use of unguided compassion-focused VR to elicit durable positive affect. Additionally, regional cultural background and prior lab affiliation emerged as moderating factors, emphasizing the need for stratified sampling and ecologically valid methods in future research.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".