Enhancing Self-Efficacy for Help-Seeking Among Transition-Aged Youth in Postsecondary Settings With Mental Health and/or Substance Use Concerns, Using Crowd-Sourced Online and Mobile Technologies: The Thought Spot Protocol
Bibliographic record
Abstract
BACKGROUND: Seventy percent of lifetime cases of mental illness emerge prior to age 24. While early detection and intervention can address approximately 70% of child and youth cases of mental health concerns, the majority of youth with mental health concerns do not receive the services they need. OBJECTIVE: The objective of this paper is to describe the protocol for optimizing and evaluating Thought Spot, a Web- and mobile-based platform cocreated with end users that is designed to improve the ability of students to access mental health and substance use services. METHODS: This project will be conducted in 2 distinct phases, which will aim to (1) optimize the existing Thought Spot electronic health/mobile health intervention through youth engagement, and (2) evaluate the impact of Thought Spot on self-efficacy for mental health help-seeking and health literacy among university and college students. Phase 1 will utilize participatory action research and participatory design research to cocreate and coproduce solutions with members of our target audience. Phase 2 will consist of a randomized controlled trial to test the hypothesis that the Thought Spot intervention will show improvements in intentions for, and self-efficacy in, help-seeking for mental health concerns. RESULTS: We anticipate that enhancements will include (1) user analytics and feedback mechanisms, (2) peer mentorship and/or coaching functionality, (3) crowd-sourcing and data hygiene, and (4) integration of evidence-based consumer health and research information. CONCLUSIONS: This protocol outlines the important next steps in understanding the impact of the Thought Spot platform on the behavior of postsecondary, transition-aged youth students when they seek information and services related to mental health and substance use.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".