A Study on the Effectiveness of I-Sparx for Nunavut Youth: Can an E-Intervention Mental Health Tool in the Form of a Computer Game Improve Emotion Regulation?
Bibliographic record
Abstract
This study examines how the e-intervention computer game, I-SPARX, may affect user emotion regulation. I-SPARX was designed for at-risk ᐃᓄᐃᑦ(Inuit) youth to teach cognitive behavioural skills and assist with improving mental health and emotional well-being. Data were collected from 112 ᐃᓄᐃᑦ (Inuit) youth participants from 25 communities in Nunavut who played I-SPARX and completed pre- and post-game outcome measures. Mixed model analysis was conducted at two time points to examine changes before and after playing the game in 6 domains meant to assess learning objectives and skills development: finding hope, being active, dealing with emotions, overcoming problems, recognizing challenging and unhelpful thoughts, and overall wellness. Results showed positive shifts in participant responses across all categories including emotion regulation skill development. This preliminary data is promising, suggesting that I-SPARX could help teach ᐃᓄᐃᑦ(Inuit) youth skills to manage emotions when they are confronted with challenging situations where negative and unhelpful thoughts arise.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".