MétaCan
Menu
Back to cohort
Record W2486136312 · doi:10.2196/games.5793

A Video Game Promoting Cancer Risk Perception and Information Seeking Behavior Among Young-Adult College Students: A Randomized Controlled Trial

2016· article· en· W2486136312 on OpenAlexvenueno aff
Georges E. Khalil, Ivan L. Beale, Minxing Chen, Alexander V. Prokhorov

Bibliographic record

VenueJMIR Serious Games · 2016
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsnot available
FundersNational Cancer InstituteUniversity at BuffaloNational Institutes of HealthDepartment of Physiology and Biophysics, University at Buffalo
KeywordsPerceptionRandomized controlled trialPsychologyVideo gameInformation seekingSocial psychologyApplied psychologyClinical psychologyGerontologyMedicineComputer scienceMultimediaInternal medicineNeuroscience

Abstract

fetched live from OpenAlex

BACKGROUND: Risky behaviors tend to increase drastically during the transition into young adulthood. This increase may ultimately facilitate the initiation of carcinogenic processes at a young age, highlighting a serious public health problem. By promoting information seeking behavior (ISB), young adults may become aware of cancer risks and potentially take preventive measures. OBJECTIVE: Based on the protection motivation theory, the current study seeks to evaluate the impact of challenge in a fully automated video game called Re-Mission on young adult college students' tendency to perceive the severity of cancer, feel susceptible to cancer, and engage in ISB. METHODS: A total of 216 young adults were recruited from a university campus, consented, screened, and randomized in a single-blinded format to 1 of 3 conditions: an intervention group playing Re-Mission at high challenge (HC; n=85), an intervention group playing Re-Mission at low challenge (LC; n=81), and a control group with no challenge (NC; presented with illustrated pictures of Re-Mission; n=50). Measurement was conducted at baseline, immediate posttest, 10-day follow-up, and 20-day follow-up. Repeated-measures mixed-effect models were conducted for data analysis of the main outcomes. RESULTS: A total of 101 young adults continued until 20-day follow-up. Mixed-effect models showed that participants in the HC and LC groups were more likely to increase in perceived susceptibility to cancer (P=.03), perceived severity of cancer (P=.02), and ISB (P=.01) than participants in the NC group. The LC group took until 10-day follow-up to show increase in perceived susceptibility (B=0.47, standard error (SE) 0.16, P=.005). The HC group showed an immediate increase in perceived susceptibility at posttest (B=0.43, SE 0.14, P=.002). The LC group exhibited no changes in perceived severity (B=0.40, SE 0.33, P=.24). On the other hand, the HC group showed a significant increase from baseline to posttest (B=0.39, SE 0.14, P=.005), maintaining this increase until 20-day follow-up (B=-0.007, SE 0.26, P=.98). Further analyses indicated that perceived threat from virtual cancer cells in the game is related to the increase in perceived severity (B=0.1, SE 0.03, P=.001), and perceived susceptibility is related to changes in ISB at 10-day follow-up (B=0.21, SE 0.08, P=.008). CONCLUSIONS: The feature of challenge with cancer cells in a virtual environment has the potential to increase cancer risk perception and ISB. The results are promising considering that the Re-Mission intervention was neither designed for cancer risk communication, nor applied among healthy individuals. Further research is needed to understand the theoretical framework underlying the effects of Re-Mission on ISB. The findings call for the development of a Web-based, game-based intervention for cancer risk communication and information seeking among young adults. CLINICALTRIAL: International Standard Randomized Controlled Trial Number (ISRCTN): 15789289; http://www.controlled-trials.com/ISRCTN15789289 (Archived by WebCite at http://www.webcitation.org/6jGYZC3lZ).

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)0.006
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation 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 score0.226
Threshold uncertainty score0.739

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.005
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.389
Teacher spread0.377 · 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.

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

Citations18
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueJMIR Serious GamesSame topicHealth Literacy and Information AccessibilityFrench-language works237,207