<i>One Night Stan:</i> Feasibility Study of an HIV Prevention and Sexual Risk Reduction Social Card Game for Young Black Women
Bibliographic record
Abstract
OBJECTIVE: To develop and test the feasibility and preliminary impact of the social card game prototype, One Night Stan, a theory-driven and evidence-based human immunodeficiency virus (HIV) prevention intervention for young black women. MATERIALS AND METHODS: The study included the enrollment of 21 young, heterosexual black women (mean age 19) to test the feasibility and preliminary impact of the card game, using a pre/postdesign. Participant satisfaction and gameplay experience were assessed using quantitative and qualitative measures. Knowledge, self-efficacy, and intentions regarding condom use and HIV/sexually transmitted infection partner testing were assessed using standardized assessments. Effect sizes for the change in these outcome variables were calculated to determine the preliminary efficacy of the game. RESULTS: One hundred percent of participants reported that that they would play the game again, 95% liked the way the game looked, 100% enjoyed playing the game, and 100% reported that they would tell their friends to play. Effect sizes were large (ranged from 0.21 to 0.51) for all variables except perceived susceptibility (0.07) and suggest that playing the game can lead to increased self-efficacy and intentions to use condoms and insist that their partners get tested for HIV across time. CONCLUSIONS: One Night Stan is a feasible intervention approach and may be efficacious in helping players develop a pattern of cognitions and motivation that can protect them against the risk of HIV.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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 source (direct Gemma or distilled Codex), 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".