An App to Help Young People Self-Manage When Feeling Overwhelmed (ReZone): Protocol of a Cluster Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: The association between behavioral difficulties and academic attainment is well established. Recent policy advising schools on managing behavior has promoted the early identification of behavioral difficulties. There is also increasing research into mHealth interventions to provide support for emotional and behavioral difficulties for young people. OBJECTIVE: The primary aim of the proposed research is to examine the effectiveness of an mHealth intervention, ReZone, in reducing emotional and behavioral difficulties in young people. METHODS: The protocol is a cluster trial of 12 classes with N=120 students with classes randomized to ReZone or management as usual. Multilevel modeling will be used to compare ReZone versus management as usual accounting for classroom-level variation. RESULTS: Baseline data collection started in February 2017 and ended in April 2017. Follow-up data collection started in April 2017 and ended in June 2017. CONCLUSIONS: The proposed research will provide evidence as to whether ReZone is effective at helping young people to self-manage when feeling overwhelmed. TRIAL REGISTRATION: ISRCTN 13425994; http://www.isrctn.com/ISRCTN13425994 (Archived by WebCite at http://www.webcitation.org/6tePwwiHk).
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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 | 0.026 | 0.027 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.085 | 0.014 |
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".