The Effect of a Health Game Prompt on Self-efficacy: Online Between-Subjects Experimental Survey
Bibliographic record
Abstract
BACKGROUND: Games for health are increasingly used as (part of) health interventions and more effect research into games for health is being done. This online experiment questions expectancies of games for health by investigating whether a game for health prompt might be considered arousal congruent cognitive reappraisal and as such positively effects self-efficacy before gameplay. OBJECTIVE: The aim of this study experiment is to test whether a game for health prompt effects self-efficacy and other well-being measurements, as a first step into investigating if a game prompt is a form of arousal congruent cognitive reappraisal. METHODS: This study used an online, 2D, between-subjects experimental survey design with self-efficacy as the main dependent variable. Stimulus is an assignment for health-related problem solving concerning living with diabetes type II, introduced as a game (n=125) versus the same assignment introduced as a task (n=107). Measurements after prompting the game/task assignment include self-efficacy, positive and negative affect, expected difficulty, flourishing, and self-esteem. RESULTS: The results indicate a small negative effect from prompting the game assignment on self-efficacy, compared with prompting a task assignment. This effect is mediated by the expected difficulty of the health game/task. No differences between the game and task groups were found in affect, flourishing, or self-esteem. CONCLUSIONS: This experiment provides no support for the notion that a game for health prompt might be seen as arousal congruent cognitive reappraisal.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".