Game playbooks: tools to guide multidisciplinary teams in developing videogame-based behavior change interventions
Bibliographic record
Abstract
As mobile technologies and videogaming platforms are becoming increasingly prevalent in the realm of health and healthcare, so are the opportunities to use these resources to conduct behavioral interventions. The creation and empirical testing of game style interventions, however, is challenged by the requisite collaboration of multidisciplinary teams, including researchers and game developers who have different cultures, terminologies, and standards of evidence. Thus, traditional intervention development tools such as logic models and intervention manuals may need to be augmented by creating what we have termed "Game Playbooks" which are intervention guidebooks that are created by, understood by, and acceptable to all members of the multidisciplinary game development team. The purpose of this paper is to describe the importance and content of a Game Playbook created to aide in the development of a videogame intervention designed specifically for health behavior change in young teens as well as the process for creating such a tool. We draw on the experience of our research and game design team to describe the critical components of the Game Playbook and the necessity of creating such a tool.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.007 |
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".