Protocol for a cluster randomized study to compare the effectiveness of a self-report distress tool and a mental health referral service to usual case management on program completion among vulnerable youth enrolled in a vocational training program
Bibliographic record
Abstract
OBJECTIVES: 1) To compare the effect of the self-report distress tool (DT) and rapid mental health referral process (MH) on vocational training program attendance. 2) To compare the effect of the DT and MH on vocational training program completion. 3) To compare the effect of the DT an MH on post-vocational training program employment. DESIGN: Pragmatic, multi-centre, 2x2 factorial, cluster randomized, superiority study with 4 parallel groups and primary endpoints of vocational program attendance and completion at 12 weeks and post-program employment at 24 months. Cluster randomization of each training cohort will be performed with a 1:1:1:1 allocation ratio using a site stratified, permuted-block group schema. Final sample size is expected to be 400 participants (100 per group). PARTICIPANTS: Students enrolled in Community Builder's Trades & Diversity Training Program in either the city of Barrie or Sudbury (in Ontario, Canada) will be eligible for enrollment if they have an active Ontario Health Insurance Plan number and Canadian Social Insurance Number and provide written informed consent prior to Training program commencement. OUTCOMES: The primary outcome includes: 1) Difference in proportion of absence-free program days from date of randomization, where absence-free days are defined as being present in class or work setting for ≥ 8 hours from Monday to Thursday during the 12-week program duration. TRIAL REGISTRATION: ClinicalTrials.gov NCT05626374 (November 23, 2022).
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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 | 0.062 | 0.044 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.004 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.104 | 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".