Young People’s Trust in Cocreated Web-Based Resources to Promote Mental Health Literacy: Focus Group Study
Bibliographic record
Abstract
BACKGROUND: There is a pressing need to create resources to promote mental health literacy among young people. Digital media is one of the methods that can be used to successfully promote mental health literacy. Although digital mental health resources are generally favorably perceived by young people, one of the essential factors in whether they choose to use these interventions is trust. OBJECTIVE: The objective of this study was to explore young people's trust-related concerns about and recommendations for the cocreated mental health website "What's Up With Everyone" by using TrustScapes. Our aim was to use the findings to improve the trustworthiness of the website and to inform future creators of web-based mental health resources. METHODS: In total, 30 young people (mean age 19, SD 1.509; range 17-21 years) participated in TrustScapes focus groups. Thematic analysis was carried out to analyze both the TrustScapes worksheets and audio transcripts. RESULTS: Qualitative analysis revealed that the mental health website contains elements perceived to be both trustworthy and untrustworthy by young people. The relatable and high-quality design, which was achieved by cocreating the website with a team of design professionals and young people, was considered to increase trust. Creators' credibility also positively affected trust, but the logos and other information about the creators were recommended to be more salient for users. Suggestions were made to update the privacy policy and cookie settings and include communication functions on the platform to improve the trustworthiness of the website. CONCLUSIONS: Factors perceived to be trustworthy included the website's relatable, high-quality design and creators' credibility, whereas those perceived to be untrustworthy included the privacy policy and cookie settings. The findings highlighted the significance of collaborating with end users and industrial partners and the importance of making the trust-enabling factors salient for users. We hope that these findings will inform future creators of web-based mental health resources to make these resources as trustworthy and effective as possible.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".