User Perspectives on a Resilience-Building App (JoyPop): Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Resilience is the capability, resources, and processes that are available to a person or system to adapt successfully in the face of stress or adversity. Given that resilience can be enhanced, using advances in technology to deliver and evaluate the impact of resilience interventions is warranted. Evidence supports the effectiveness of the resilience-building JoyPop app in improving resilience-related outcomes after use; however, experiential data from users is also needed to provide a more comprehensive account of its utility. OBJECTIVE: The aim of this study was to explore users' experiences with the JoyPop app and their perspectives on its utility. METHODS: This qualitative description study involved a combination of group and one-on-one semistructured interviews with a subset of first-year undergraduate students who participated in a larger evaluation of the JoyPop app. Participants used the app for a 4-week period and were subsequently asked about their frequency of app use, most and least used features (and associated reasons), most and least helpful features (and associated reasons), barriers to use, facilitators of use and continuation, and recommendations for improvement. Data were coded and categorized through inductive content analysis. RESULTS: The sample of 30 participants included 24 females and 6 males, with a mean age of 18.77 years (SD 2.30). App use ranged from 1 to 5 times daily (mean 2.11, SD 0.74), with the majority indicating that they used the app at least twice daily. The Rate My Mood, Journal, and SquareMoves features were reported to be used most often, while the Rate My Mood, Journal, and Breathing Exercises features were identified as the most helpful. A number of themes and subthemes pertaining to facilitators of app use (prompts, creating routine, self-monitoring opportunities, expressive opportunities), barriers to app use (editing, lack of variety, student lifestyle), outcomes of app use (increased awareness, checking in with oneself, helpful distraction, emotional control), and recommendations for app improvement (adding more features, enhancing existing features, enhancing tracking abilities, providing personalization) were identified. CONCLUSIONS: This study provides insight into the aspects of the JoyPop app that motivated and benefitted users, as well as measures that can be taken to improve user experiences and promote longer-term uptake. Users were willing to engage with the app and incorporate it into their routine, and they valued the ability to self-monitor, express emotion, and engage in distraction.
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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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".