Who wants to catch 'em all? Perceptions of Pokémon Go in game users and non-users
Bibliographic record
Abstract
Pokemon Go is an augmented-reality game in which players move around their community catching monsters, acquiring supplies, and battling opposing teams. Both health researchers and the popular media have identified Pokemon Go as a high-impact health promotion tool, with the ability to increase physical activity and prompt community engagement. However, empirical research is lacking on the motivational factors that draw people toward Pokemon Go. Answers may provide interventionists with insights into the individual-level factors associated with technological uptake. Our purpose was to investigate how Pokemon Go users (N=448) and non-users (N=166) differ in their perceptions of Pokemon Go and physical activity and game use. Participants completed an online questionnaire that examined the social cognitive constructs of barriers, outcome expectancies (motivation), and self-efficacy. Non-users primarily identified not having enough time as a barrier to game uptake. Wilk's statistic indicated significant differences between users and non-users in Pokemon Go outcome expectancies, p
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".