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Record W3025518109 · doi:10.2196/18207

Developing a Theory-Driven Serious Game to Promote Prescription Opioid Safety Among Adolescents: Mixed Methods Study

2020· article· en· W3025518109 on OpenAlexvenueno aff
Olufunmilola Abraham, Tanvee Thakur, Randall Brown

Bibliographic record

VenueJMIR Serious Games · 2020
Typearticle
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsnot available
FundersWisconsin Center for Education Research, School of Education, University of Wisconsin-MadisonNational Institutes of HealthNational Center for Advancing Translational SciencesUniversity of Wisconsin-Madison
KeywordsMedical prescriptionThematic analysisConceptual frameworkMedical educationGame DeveloperConceptual modelPsychologyThe Conceptual FrameworkGame designMedicineApplied psychologyComputer scienceNursingQualitative researchMultimediaSociology

Abstract

fetched live from OpenAlex

BACKGROUND: Adolescents in North America are severely affected by the opioid crisis, yet there are limited educational resources for educating teens about prescription opioid safety and misuse. Empirical literature lacks evidence regarding teen education about prescription opioid safety through serious games and lacks conceptual models and frameworks to guide the process of game development for this purpose. OBJECTIVE: This study aims to conceptualize and design a serious game prototype to teach teens about prescription opioid safety and propose a conceptual framework for developing a serious game to educate youth about safe and responsible use of prescription opioids. METHODS: The initial steps of the project comprised of the formulation of an integrated conceptual framework that included factors from health behavior models and game development models. This was followed by the formal process of serious game development, which resulted in a game prototype. The assessment of the game prototype was done through group discussions, individual interviews, and questionnaires with adolescents following gameplay. Field notes were used to keep track of the responses from the group discussions. Content and thematic analyses were used to analyze field notes and responses to the open-ended questionnaire, which were then used to refine the game prototype. RESULTS: A total of 10 playtests with over 319 adolescents and emerging young adults (AYAs) in community settings such as middle schools, high schools, and colleges were conducted by the project team between March and June 2019. The AYAs provided feedback on the initial game prototype using questionnaires administered through Qualtrics or in-person on paper. Preliminary feedback suggested that the teens found the game objectives, outcomes, and design appealing. Overall, the game was perceived as realistic, and learning outcomes seemed achievable. Suggestions for improvement included the need for additional direction on gameplay, clearer instructions, concise dialog, and reduced technical problems in the gameplay. CONCLUSIONS: We propose a conceptual framework for developing a serious game prototype to educate youth about prescription opioid safety. The project used a theory-driven conceptual framework for the development of a serious game targeting the prevention of adolescent opioid misuse and garnered preliminary feedback on the game to improve the quality of gameplay and the prototype. Feedback through informal assessments in community settings suggests that the youth and their families are interested in a game-based approach to learn about prescription opioid safety in homes and schools. The next steps include modifications to the game prototype based on feedback from the community, integration of learning analytics to track the in-game behaviors of players, and formal testing of the final prototype.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.834
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.031
GPT teacher head0.374
Teacher spread0.344 · 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 teacher head, not a consensus.

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

Citations26
Published2020
Admission routes1
Has abstractyes

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