Perspectives of Family Members on Using Technology in Youth Mental Health Care: A Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Information and communication technologies (ICTs) are increasingly recognized as having an important role in the delivery of mental health services for youth. Recent studies have evaluated young people's access and use of technology, as well as their perspectives on using technology to receive mental health information, services, and support; however, limited attention has been given to the perspectives of family members in this regard. OBJECTIVE: The aim of this study was to explore the perspectives of family members on the use of ICTs to deliver mental health services to youth within the context of specialized early intervention for a first-episode psychosis (FEP). METHODS: Six focus groups were conducted with family members recruited from an early intervention program for psychosis. Twelve family members participated in the study (target sample was 12-18, and recruitment efforts took place over the duration of 1 year). A 12-item semistructured focus group guide was developed to explore past experiences of technology and recommendations for the use of technology in youth mental health service delivery. A qualitative thematic analysis guided the identification and organization of common themes and patterns identified across the dataset. RESULTS: Findings were organized by the following themes: access and use of technology, potential negative impacts of technology on youth in recovery, potential benefits of using technology to deliver mental health services to youth, and recommendations to use technology for (1) providing quality information in a manner that is accessible to individuals of diverse socioeconomic backgrounds, (2) facilitating communication with health care professionals and services, and (3) increasing access to peer support. CONCLUSIONS: To our knowledge, this is among the first (or the first) to explore the perspectives of family members of youth being treated for FEP on the use of technology for mental health care. Our results highlight the importance of considering diverse experiences and attitudes toward the role of technology in youth mental health, digital literacy skills, phases of recovery, and sociodemographic factors when engaging family members in technology-enabled youth mental health care research and practice. Innovative methods to recruit and elicit the perspectives of family members on this topic are warranted. It is also important to consider educational strategies to inform and empower family members on the role, benefits, and use of ICTs in relation to mental health care for FEP.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 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) | 0.004 | 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".