Supplementary file 1_Acceptance of a mental health app (JoyPopTM) for postsecondary students: a prospective evaluation using the UTAUT2.pdf
Bibliographic record
Abstract
Introduction Mental health (MH) smartphone applications (MH apps) can support the increasing MH needs of postsecondary students and mitigate barriers to accessing support. Evaluating MH app acceptance using technology acceptance models is recommended to improve student engagement with MH apps. The JoyPop<sup>TM</sup> app was designed to improve youth resilience and emotion regulation. The JoyPop<sup>TM</sup> app is associated with improved student MH, but its acceptance has yet to be evaluated quantitatively. The present study used the Unified Theory of Acceptance and Use of Technology (UTAUT2) to evaluate and examine constructs and moderators influencing the acceptance (i.e., behavioural intention) and use of the JoyPop<sup>TM</sup> app. Method Participants were 183 postsecondary students attending a Canadian University who used the app for one week and completed measures before and after using the app. Relationships posited by the UTAUT2 were tested using partial least squares structural equation modelling (PLS-SEM). Results Most participants accepted the JoyPop<sup>TM</sup> app. The UTAUT2 model explained substantial variance in behavioural intention and app use. Performance expectancy, hedonic motivation, and facilitating conditions predicted behavioural intention, and behavioural intention and facilitating conditions predicted app use. Age moderated the association between facilitating conditions and behavioural intention. Experience moderated the relationship between performance expectancy, hedonic motivation, and social influence on behavioural intention. Discussion Results provide insight into factors influencing the acceptance of the JoyPop<sup>TM</sup> app and its ability to engage students. Results also provide valuable insights for evaluating and optimally designing MH apps.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.183 | 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 teacher head, 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".