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Record W4387915006 · doi:10.2196/48401

Effects of a Preventive Mental Health Curriculum Embedded Into a Scholarly Gaming Course on Adolescent Self-Esteem: Prospective Matched Pairs Experiment

2023· article· en· W4387915006 on OpenAlexvenueno aff
Christopher G Jenson, Sharon Fitzgerald Wolff, Libby Matile Milkovich

Bibliographic record

VenueJMIR Serious Games · 2023
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsnot available
FundersUniversity of Florida HealthFlorida Department of Health
KeywordsPsychologyCurriculumSelf-esteemMental healthCourse (navigation)Medical educationClinical psychologyDevelopmental psychologyPsychotherapistMedicinePedagogyEngineering

Abstract

fetched live from OpenAlex

Background: Positive self-esteem predicts happiness and well-being and serves as a protective factor for favorable mental health. Scholarly gaming within the school setting may serve as a channel to deliver a mental health curriculum designed to improve self-esteem. Objective: This study aims to evaluate the impact of a scholarly gaming curriculum with and without an embedded preventive mental health curriculum, Mental Health Moments (MHM), on adolescents' self-esteem. Methods: The scholarly gaming curriculum and MHM were developed by 3 educators and a school-based health intervention expert. The scholarly gaming curriculum aligned with academic guidelines from the International Society for Technology Education, teaching technology-based career skills and video game business development. The curriculum consisted of 40 lessons, delivered over 14 weeks for a minimum of 120 minutes per week. A total of 83 schools with previous gaming engagement were invited to participate and 34 agreed. Schools were allocated to +MHM or -MHM arms through a matched pairs experimental design. The -MHM group received the scholarly gaming curriculum alone, whereas the +MHM group received the scholarly gaming curriculum plus MHM embedded into 27 lessons. MHM integrated concepts from the PERMA framework in positive psychology as well as the Collaborative for Academic, Social, and Emotional Learning (CASEL) standards in education, which emphasize self-awareness, self-management, social awareness, relationship skills, and responsible decision-making. Participants in the study were students at schools offering scholarly gaming curricula and were enrolled at recruitment sites. Participants completed a baseline and postintervention survey quantifying self-esteem with the Rosenberg Self-Esteem Scale (score range 0-30). A score <15 characterizes low self-esteem. Participants who completed both baseline and postintervention surveys were included in the analysis. Results: Of the 471 participants included in the analysis, 235 received the -MHM intervention, and 236 received the +MHM intervention. Around 74.9% (n=353) of participants were in high school, and most (n=429, 91.1%) reported this was their first year participating in scholarly gaming. Most participants were male (n=387, 82.2%). Only 58% (n=273) reported their race as White. The average self-esteem score at baseline was 17.9 (SD 5.1). Low self-esteem was reported in 22.1% (n=104) of participants. About 57.7% (n=60) of participants with low self-esteem at baseline rated themselves within the average level of self-esteem post intervention. When looking at the two groups, self-esteem scores improved by 8.3% among the +MHM group compared to no change among the -MHM group (P=.002). Subgroup analyses revealed that improvements in self-esteem attributed to the +MHM intervention differed by race, gender, and sexual orientation. Conclusions: Adolescents enrolled in a scholarly gaming curriculum with +MHM had improved self-esteem, shifting some participants from abnormally low self-esteem scores into normal ranges. Adolescent advocates, including health care providers, need to be aware of nontraditional educational instruction to improve students' well-being.

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)0.005
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.001

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 head0.011
GPT teacher head0.365
Teacher spread0.354 · 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 designObservational
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

Citations2
Published2023
Admission routes1
Has abstractyes

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