The Optimization of an eHealth Solution (Thought Spot) with Transition-Aged Youth in Postsecondary Settings: Participatory Design Research
Bibliographic record
Abstract
Background: Seventy percent of lifetime cases of mental illness emerge before the age of 24 years, but many youth are unable to access the support and services they require in a timely and appropriate way.With most youth using the internet, electronic health (eHealth) interventions are promising tools for reaching this population.Through participatory design research (PDR) engagement methods, Thought Spot, a Web-and mobile-based platform, was redeveloped to facilitate access to mental health services by transition-aged youth (aged 16-29 years) in postsecondary settings. Objective:The aim of this study was to describe the process of engaging with postsecondary students through the PDR approaches, with the ultimate goal of optimizing the Thought Spot platform.Methods: Consistent with the PDR approaches, five student-led workshops, attended by 41 individuals, were facilitated to obtain feedback regarding the platform's usability and functionality and its potential value in a postsecondary setting.Various creative engagement activities were delivered to gather experiences and opinions, including semistructured focus groups, questionnaires, personas, journey mapping, and a world café.Innovative technological features and refinements were also brainstormed during the workshops.Results: By using PDR methods of engagement, participants knew that their ideas and recommendations would be applied.There was also an overall sense of respect and care integrated into each group, which facilitated an exchange of ideas and suggestions.
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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 | 0.059 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".