Identifying the Minimal Clinically Important Difference in Emotion Regulation Among Youth Using the JoyPop App: Survey Study
Bibliographic record
Abstract
BACKGROUND: The minimal clinically important difference (MCID) is an important threshold to consider when evaluating the meaningfulness of improvement following an intervention. The JoyPop app is an evidence-based smartphone app designed to improve resilience and emotion regulation. Information is needed regarding the JoyPop app's MCID among culturally diverse youth. OBJECTIVE: This study aims to calculate the MCID for youth using the JoyPop app and to explore how the MCID may differ for a subset of Indigenous youth. METHODS: Youth (N=36; aged 12-18 years) were recruited to use the JoyPop app for up to 4 weeks as part of a larger pilot evaluation. Results were based on measures completed after 2 weeks of app use. The MCID was calculated using emotion regulation change scores (Difficulties in Emotion Regulation-Short Form [DERS-SF]) and subjective ratings on the Global Rating of Change Scale (GRCS). This MCID calculation was completed for youth overall and separately for Indigenous youth only. RESULTS: A significant correlation between GRCS scores and change scores on the DERS-SF supported face validity (r=-0.37; P=.04). The MCID in emotion regulation following the use of the JoyPop app for youth overall was 2.80 on the DERS-SF. The MCID for Indigenous youth was 4.29 on the DERS-SF. In addition, most youth reported improved emotion regulation after using the JoyPop app. CONCLUSIONS: These MCID findings provide a meaningful threshold for improvement in emotion regulation for the JoyPop app. They provide potential effect sizes and can aid in sample size estimations for future research with the JoyPop app or e-mental health technologies in general. The difference between overall youth and Indigenous youth MCID values also highlights the importance of patient-oriented ratings of symptom improvement as well as cultural considerations when conducting intervention research and monitoring new interventions in clinical practice.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".