Pilot testing a computerized CBT program in a remote Arctic region: Nunavut youth and youth workers reflect on SPARX
Bibliographic record
Abstract
Background: Depression and suicidality amongst Inuit youth is one of the most significant health concerns in Nunavut (Canada), however access to mental health services remains limited (Kirmayer et al., 2000). It is thus crucial to explore innovative intervention modalities for this community. Evidence suggests that Computerized Cognitive Behavioural Therapy (cCBT) may be effective for depressed adolescents and young adults (Andrews et al., 2010; Richardson, Stallard, and Velleman, 2010). SPARX is a form of cCBT that has been shown to reduce symptoms of depression and anxiety in indigenous youth of New Zealand (Merry et al., 2012; Shepherd et al., 2015). This presentation presents qualitative data derived from a recently completed pilot evaluation of SPARX in 25 remote Inuit communities in Nunavut. \n \nMethod: Participants: A sub-sample of youth participants from the study (12 of a total of 49), who completed the SPARX program, and 8 community workers from Nunavut who had supervised the youth during their SPARX trial. Procedure: Participants completed phone-based focus group interviews about their experiences of SPARX; whether they would recommend the program to others and why they might do so; how they would describe the program to others; any changes they would like to see made to SPARX; and how the game could be rendered more culture-specific. Analysis: Thematic analysis was used to analyze focus group data (Braun & Clarke, 2006). \n \nResults: Feedback from youth who completed SPARX, and youth workers who supervised them, was overwhelmingly positive. Five themes were identified based on youth feedback: SPARX promotes emotional regulation; SPARX skills can be applied to daily life; SPARX should be recommended to peers; SPARX should be modified to be Inuit-specific; SPARX could be improved. Four further themes were identified based on youth worker feedback: Positive changes in youth who had completed SPARX; Need for access to SPARX for all youth; SPARX should be modified to be Inuit-specific; Systemic challenges encountered when administering SPARX should be addressed. \n \nConclusion: SPARX is a novel computer-based program designed to foster resilience and protect against depression. SPARX is showing promise among some of Canada’s most vulnerable youth. There is currently a push for the provision of culturally-sensitive, accessible mental health services. Once culturally adapted, SPARX may be a useful tool for implementation in remote, often marginalized communities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".