A Web- and Mobile-Based Map of Mental Health Resources for Postsecondary Students (Thought Spot): Protocol for an Economic Evaluation (Preprint)
Bibliographic record
Abstract
BACKGROUND Youth demonstrate a low propensity to seek help for mental health issues and exhibit low use of health services despite the high prevalence of mental health challenges in this population. Research has found that delivering interventions via the internet and mobile devices is an effective way to reach youth. Thought Spot, a Web- and mobile-based map, was developed to help transition-aged youth in postsecondary settings overcome barriers to help-seeking, thereby reducing the economic burden associated with untreated mental health issues. OBJECTIVE This paper presents the protocol for an economic evaluation that will be conducted in conjunction with a randomized controlled trial (RCT) to evaluate the effectiveness and cost of Thought Spot compared with usual care in terms of self-efficacy for mental health help-seeking among postsecondary students. METHODS A partially blinded RCT will be conducted to assess the impact of Thought Spot on the self-efficacy of students for mental health help-seeking. Students from 3 postsecondary institutions in Ontario, Canada will be randomly allocated to 1 of 2 intervention groups (resource pamphlet or Thought Spot) for 6 months. The economic evaluation will focus on the perspective of postsecondary institutions or other organizations interested in using Thought Spot. Costs and resources for operating and maintaining the platform will be reported and compared with the costs and resource needs associated with usual care. The primary outcome will be change in help-seeking intentions, measured using the General Help-Seeking Questionnaire. The cost-effectiveness of the intervention will be determined by calculating the incremental cost-effectiveness ratio, which will then be compared with willingness to pay. RESULTS The RCT is scheduled to begin in February 2018 and will run for 6 months, after which the economic evaluation will be completed. CONCLUSIONS We expect to demonstrate that Thought Spot is a cost-effective way to improve help-seeking intentions and encourage help-seeking behavior among postsecondary students. The findings of this study will help inform postsecondary institutions when they are allocating resources for mental health initiatives. CLINICALTRIAL ClinicalTrials.gov NCT03412461; https://clinicaltrials.gov/ct2/show/NCT03412461 (Archived at WebCite at http://www.webcitation.org/6xy5lWpnZ)
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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 | 0.050 | 0.068 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.140 | 0.018 |
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".