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Record W4313027970 · doi:10.2196/38493

Evaluating the Utility of a Psychoeducational Serious Game (SPARX) in Protecting Inuit Youth From Depression: Pilot Randomized Controlled Trial

2022· article· en· W4313027970 on OpenAlexaffvenueabout
Yvonne Bohr, Leah Litwin, Jeffrey Hankey, Hugh McCague, Chelsea Singoorie, Mathijs Lucassen, Matthew Shepherd, Jenna Barnhardt

Bibliographic record

VenueJMIR Serious Games · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsNunavut Research InstituteYork University
Fundersnot available
KeywordsMental healthMoodPsychologyRandomized controlled trialIntervention (counseling)Clinical psychologyMedicinePsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Inuit youth in Northern Canada show considerable resilience in the face of extreme adversities. However, they also experience significant mental health needs and some of the highest adolescent suicide rates in the world. Disproportionate rates of truancy, depression, and suicide among Inuit adolescents have captured the attention of all levels of government and the country. Inuit communities have expressed an urgent imperative to create, or adapt, and then evaluate prevention and intervention tools for mental health. These tools should build upon existing strengths, be culturally appropriate for Inuit communities, and be accessible and sustainable in Northern contexts, where mental health resources are often scarce. OBJECTIVE: This pilot study assesses the utility, for Inuit youth in Canada, of a psychoeducational e-intervention designed to teach cognitive behavioral therapy strategies and techniques. This serious game, SPARX, had previously demonstrated effectiveness in addressing depression with Māori youth in New Zealand. METHODS: The Nunavut Territorial Department of Health sponsored this study, and a team of Nunavut-based community mental health staff facilitated youth's participation in an entirely remotely administered pilot trial using a modified randomized control approach with 24 youths aged 13-18 across 11 communities in Nunavut. These youth had been identified by the community facilitators as exhibiting low mood, negative affect, depressive presentations, or significant levels of stress. Entire communities, instead of individual youth, were randomly assigned to an intervention group or a waitlist control group. RESULTS: Mixed models (multilevel regression) revealed that participating youth felt less hopeless (P=.02) and engaged in less self-blame (P=.03), rumination (P=.04), and catastrophizing (P=.03) following the SPARX intervention. However, participants did not show a decrease in depressive symptoms or an increase in formal resilience indicators. CONCLUSIONS: Preliminary results suggest that SPARX may be a good first step for supporting Inuit youth with skill development to regulate their emotions, challenge maladaptive thoughts, and provide behavioral management techniques such as deep breathing. However, it will be imperative to work with youth and communities to design, develop, and test an Inuit version of the SPARX program, tailored to fit the interests of Inuit youth and Elders in Canada and to increase engagement and effectiveness of the program. TRIAL REGISTRATION: ClinicalTrials.gov NCT05702086; https://www.clinicaltrials.gov/ct2/show/NCT05702086.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.986
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0100.001

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.

Opus teacher head0.050
GPT teacher head0.388
Teacher spread0.339 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreEmpirical

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".

Quick stats

Citations16
Published2022
Admission routes3
Has abstractyes

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