Apprentices’ Attitudes Toward Using a Mental Health Mobile App to Support Healthy Coping: Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Apprenticeships are a common pathway for young people transitioning into the workforce. Apprentices often face many employment-related challenges and have high levels of psychological distress, drug and alcohol use, and suicidal ideation. Little is known about the attitudes of apprentices toward using smartphone apps to support their mental health and the content that would engage them. OBJECTIVE: This study explored (1) apprentices' interest in using an app to support their mental health and (2) the healthy coping strategies used to manage their mental well-being in the face of workplace challenges, in order to inform future app content. METHODS: A mixed methods study was conducted with 54 apprentices (50/54 male, 93%) with a mean age of 22.7 (SD 5.7) years. Participants completed a survey on preferred ways of using an app to support mental health. Across 8 focus groups, participants were asked to describe healthy strategies they used to cope with occupational stressors. RESULTS: Only 11% (6/54) of participants currently used a well-being app, but there was high interest in using an app to support their friends (47/54 participants, 87%) and develop self-help strategies to manage or prevent mental health issues (42/54 participants, 78%). Four major types of coping behaviors were identified: (1) social connection for disclosure, advice, and socializing; (2) pleasurable activities, such as engaging in hobbies, time-outs, and developing work-life separation; (3) cognitive approaches, including defusing from thoughts and cognitive reframing; and (4) self-care approaches, including exercise, a healthy diet, and getting adequate sleep. CONCLUSIONS: There is interest among apprentices to use an app with a positive well-being focus that helps them to develop self-management skills and support their friends. Apprentices utilized a range of healthy behaviors to cope with workplace stressors that can be incorporated into mental health apps to improve uptake and engagement. However, many of the preferred coping strategies identified are not those focused on by currently available apps, indicating the need for more targeted digital interventions for this group.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".