Understanding youths' lived experience of digital mental health interventions through a photovoice approach
Bibliographic record
Abstract
Youth can face mental health challenges in the transition from adolescence to adulthood. Web-based programs, apps, and websites for mental health are increasingly developed for youth due to their consistent use of online and mobile technologies. These resources have several benefits including easy access to information, anonymity, and low cost. This study sought to develop an understanding of youth’s experiences with web-based programs, apps, and websites for mental health. Nine participants between the ages of 15-21 with lived experience of a mental health concern from British Columbia and Ontario were recruited for this study. The photovoice method was used to aid participants in exploring their experiences with digital mental health interventions through photography. Group workshops and individual interviews allowed youth to reflect on their photographs. \n \nData analysis was conducted through open inductive coding applied to transcripts from workshops and interviews. Themes related to youths’ digital mental health intervention use journeys were identified as (1) Searching for support, (2) Individual needs unmet, and (3) Finding relief. Photovoice provided a suitable research method for prioritizing youth lived expertise due to its flexible and adaptable approach. Mental health researchers and service providers should readily consider the experiences of youth when designing, selecting, and assessing digital mental health interventions as part of an integrative approach to mental health care.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".