The Design and Relevance of a Computerized Gamified Depression Therapy Program for Indigenous Māori Adolescents
Bibliographic record
Abstract
BACKGROUND: Depression is a major health issue among Māori indigenous adolescents, yet there has been little investigation into the relevance or effectiveness of psychological treatments for them. Further, consumer views are critical for engagement and adherence to therapy. However, there is little research regarding indigenous communities' opinions about psychological interventions for depression. OBJECTIVE: The objective of this study was to conduct semistructured interviews with Māori (indigenous New Zealand) young people (taitamariki) and their families to find out their opinions of a prototype computerized cognitive behavioral therapy (cCBT) program called Smart, Positive, Active, Realistic, X-factor thoughts (SPARX), a free online computer game intended to help young persons with mild to moderate depression, feeling down, stress or anxiety. The program will teach them how to resolve their issues on their own using Cognitive Behavioural Therapy as psychotherapeutic approach. METHODS: There were seven focus groups on the subject of the design and cultural relevance of SPARX that were held, with a total of 26 participants (19 taitamarki, 7 parents/caregivers, all Māori). There were five of the groups that were with whānau (family groups) (n=14), one group was with Māori teenage mothers (n=4), and one group was with taitamariki (n=8). The general inductive approach was used to analyze focus group data. RESULTS: SPARX computerized therapy has good face validity and is seen as potentially effective and appealing for Māori people. Cultural relevance was viewed as being important for the engagement of Māori young people with SPARX. Whānau are important for young peoples' well-being. Participants generated ideas for improving SPARX for Māori and for the inclusion of whānau in its delivery. CONCLUSIONS: SPARX computerized therapy had good face validity for indigenous young people and families. In general, Māori participants were positive about the SPARX prototype and considered it both appealing and applicable to them. The results of this study were used to refine SPARX prior to it being delivered to taitamariki and non-Māori young people. TRIAL REGISTRATION: The New Zealand Northern Y Regional Ethics Committee; http://ethics.health.govt.nz/home; NTY/09/003; (Archived by WebCite at http://www.webcitation/6VYgHXKaR).
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".