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Record W4220849304 · doi:10.2196/33693

Online Knowledge Translation Program Involving Video Games and University Student–Led Tutorials About Cannabis and Psychosis for Black Youth: Mixed Method Feasibility Study

2022· article· en· W4220849304 on OpenAlexafffundvenueabout
Payal Jani, Nuri Song, Erin Artna, Joonsoo Sean Lyeo, Ashley Assam, Faith Maelzer, Andriene Murphy, Angelo Grant, Suzanne Archie

Bibliographic record

VenueJMIR Formative Research · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsMcMaster University
FundersMitacsOntario Trillium FoundationMcMaster University
KeywordsCannabisPsychologyPsychosisMedical educationMental healthEffects of cannabisClinical psychologyPsychiatryApplied psychologyMedicine

Abstract

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BACKGROUND: We have piloted a new online knowledge translation (KT) program created to educate youth about cannabis effects, which uniquely focuses on mental health risks for Black youth. Youth are generally unaware of the research linking underage usage and the risk of psychosis. Youth from some Black racialized communities in Ontario may be disproportionately affected and in need of this knowledge. OBJECTIVE: Because very little is known about the acceptability and feasibility of programs educating Black youth about cannabis and psychosis risk, we evaluated this KT program, which consists of tutorials facilitated by university students and video games. METHODS: This mixed methods pilot study evaluates the transfer of knowledge about cannabis and psychosis risk before and after the online KT program and, at the same time, explores participant satisfaction with the program and views about underage use. Eligible participants were youth 16-19 years of age of Black African or Caribbean descent. Trained undergraduate students from McMaster University administered a quiz (psychosis and cannabis test; PCT) to evaluate knowledge before and after the KT program. After playing the psychoeducational video games, participants attended two tutorial group sessions led by undergraduate students. The undergraduate students facilitated the online tutorials about cannabis and psychosis. The tutorials augmented the educational content embedded within the gameplay: participants discussed what they learned from the video games and their understanding of psychosis and the effects of cannabis. In addition, undergraduate students qualitatively analyzed the tutorial discussions for themes, and the prequiz and postquiz scores were analyzed for significant differences in scores. RESULTS: A total of 9 Black youth were recruited and completed this pilot study. The mean PCT scores were 5.67 (SD 1.7) and 7.78 (SD 1.8) before and after the KT program, respectively. There was a significant improvement in scores (P<.05) post-KT program. Thematic analysis of the facilitated tutorials revealed three major themes: video game satisfaction, marijuana and psychosis literacy, and help-seeking awareness. Overall, participants showed an increased awareness and understanding of the subject matter after the gameplay and tutorial intervention. CONCLUSIONS: When supplemented with tutorial sessions, the Back to Reality Series shows promise for addressing the gap in knowledge about cannabis and psychosis, and the results provide preliminary evidence that the games appeal to Black youth.

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)0.019
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.099

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0040.001
Scholarly communication0.0020.002
Open science0.0020.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.170
GPT teacher head0.517
Teacher spread0.347 · 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 designQualitative
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

Citations7
Published2022
Admission routes4
Has abstractyes

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