Female youth and mental health service providers' perspectives on the JoyPop™ app: a qualitative study
Bibliographic record
Abstract
Introduction Mobile health (mHealth) apps are a promising adjunct to traditional mental health services, especially in underserviced areas. Developed to foster resilience in youth, the JoyPop™ app has a growing evidence base showing improvement in emotion regulation and mental health symptoms among youth. However, whether this novel technology will be accepted among those using or providing mental health services remains unknown. This study aimed to evaluate the JoyPop™ app's acceptance among (a) a clinical sample of youth and (b) mental health service providers. Method A qualitative descriptive approach involving one-on-one semi-structured interviews was conducted. Interviews were guided by the Technology Acceptance Model and were analyzed using a deductive-inductive content analysis approach. Results All youth ( n = 6 females; M age = 14.60, range 12–17) found the app easy to learn and use and expressed positive feelings towards using the app. Youth found the app useful because it facilitated accessibility to helpful coping skills (e.g., journaling to express their emotions; breathing exercises to increase calmness) and positive mental health outcomes (e.g., increased relaxation and reduced stress). All service providers ( n = 7 females; M age = 43.75, range 32–60) perceived the app to be useful and easy to use by youth within their services and expressed positive feelings about integrating the app into usual care. Service providers also highlighted various organizational factors affecting the app's acceptance. Youth and service providers raised some concerns about apps in general and provided recommendations to improve the JoyPop™ app. Discussion Results support youth and service providers' acceptance of the JoyPop™ app and lend support for it as an adjunctive resource to traditional mental health services for youth with emotion regulation difficulties.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".