The Impact of a Mental Health Game (eQuoo) on the Resilience of Young Adults: A Case Series Study
Bibliographic record
Abstract
Background: mHealth apps are showing promise as an accessible means to improve mental health and wellbeing. However, there is limited evidence for their efficacy, particularly in periods after their initial usage, and in non-Western cultures. Objective: In this study, we explored the impact of eQuoo, an emotional fitness application which gamifies self-reflection and learning, in terms of its ability to build resilience in a sample of young people in Vietnam. Materials and Methods: Individuals (n = 264, M = 25.65 years, SD = 4.84; 52% female) completed self-reports consisting of three different measures of resilience (the Rugged Resilience Measure, Adult Resilience Measure, Brief Resilience Scale). Assessments were taken at the start and end of a five-week use period, and also three months after baseline. Results: Comparison tests indicated various improvements in resilience between baseline and five weeks and at three months, depending on the subgroup of participants (whether male or female or younger or older), as well as in terms of the way resilience was operationalised (e.g., ability to ‘bounce back’ or the protective factors associated with managing adversity). Conclusion: The study indicates that eQuoo can build resilience and can therefore provide a convenient means of supporting the mental health and wellbeing of young adults.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".