Identifying Commercial Games with Therapeutic Potential through a Content Analysis of Steam Reviews
Bibliographic record
Abstract
While evidence supports that some commercial off-the-shelf video games may promote mental wellbeing, it is an extensive time investment to experimentally identify games that benefit players. The time delay between commercial games research and commercial game development can render such research out-of-date. In this work, we explore player-written game reviews as a way to expeditiously identifying games with potential benefits for mental wellbeing. Through a content analysis of review data, we found that players publicly disclose experiences consistent with self-care. Our analysis generated categories related to coping and recovery, emotional regulation, social connectedness, and obsessive play. Through this process, we identified several games as strong candidates for further research. Our work contributes to an emerging research agenda of commercial video games as therapy (VGTx), by providing a technique for rapidly identifying games with therapeutic potential. Further, we demonstrate that Steam user reviews are a valuable source of affective player experience data-a contribution with broad implications for player experience research.
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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 | 0.011 | 0.085 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".