Evaluation of an AIDS Educational Mobile Game (AIDS Fighter · Health Defense) for Young Students to Improve AIDS-Related Knowledge, Stigma, and Attitude Linked to High-Risk Behaviors in China: Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: The AIDS epidemic among young students is serious, and effective preventive interventions are urgently needed. Game-based intervention has become an innovative way to change healthy behaviors, and we have developed an AIDS educational game called AIDS Fighter · Health Defense. OBJECTIVE: In this study, we tested the effect of AIDS Fighter · Health Defense on young students in improving AIDS-related knowledge, stigma, and attitude related to high-risk behaviors in Southwest China. METHODS: A randomized controlled trial was conducted from September 14 to 27, 2020. In total, 96 students from 2 classes in a middle school were selected by stratified cluster sampling in Luzhou City, Southwest China. The students were randomly divided into the intervention group (n=50, 52%) and the control group (n=46, 48%). The intervention group played the AIDS educational game AIDS Fighter · Health Defense; the control group learned AIDS-related knowledge through independent learning on the QQ chat group. An AIDS-related knowledge questionnaire, a stigma scale, and an attitude questionnaire on AIDS-related high-risk behaviors were used to measure the effect of the AIDS educational game via face-to-face interviews. The user experience of the game was assessed using the Educational Game User Experience Evaluation Scale. The difference was statistically significant at P≤.05. RESULTS: After the intervention, the AIDS knowledge awareness rate (X̅ [SD], %) of the intervention and control groups were 70.09 (SD 11.58) and 57.49 (SD 16.58), with t=4.282 and P<.001. The stigma scores of the 2 groups were 2.44 (SD 0.57) and 2.48 (SD 0.47), with t=0.373 and P=0.71. The positive rate (X̅ [SD], %) of attitudes of high-risk AIDS behaviors of the 2 groups were 82.00 (SD 23.44) and 79.62 (SD 17.94), with t=0.555 and P=0.58. The mean percentage of the game evaluation was 54.73% as excellent, 31.45% as good, 13.09% as medium, and 0.73% as poor. CONCLUSIONS: AIDS Fighter · Health Defense could increase AIDS-related knowledge among young students, but the effect of the game in reducing AIDS-related stigma and improving the attitudes of high-risk AIDS behaviors was not seen. Long-term effects and large-scale studies are needed to assess the efficacy of game-based intervention. TRIAL REGISTRATION: Chinese Clinical Trial Registry ChiCTR2000038230; https://trialsearch.who.int/Trial2.aspx?TrialID=ChiCTR2000038230.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".