Acceptability of a Plasticity-Focused Serious Game Intervention for Posttraumatic Stress Disorder: User Requirements Analysis
Bibliographic record
Abstract
BACKGROUND: Trauma-focused cognitive behavioral therapy (TF-CBT) is a first-line treatment for posttraumatic stress disorder (PTSD). Despite a solid evidence base, TF-CBT response and attrition rates vary considerably. Plasticity-focused interventions, including the use of serious games, have the potential to improve TF-CBT response and treatment retention. OBJECTIVE: The aim of this study was to assess the acceptability of a mobile phone-delivered plasticity-focused serious game to improve response to TF-CBT for PTSD, and carry out a user requirements analysis should the development of a prototype be warranted. METHODS: We conducted 2 one-to-one interviews (n=2), one focus group involving service users who had received a diagnosis of PTSD (n=3) and one focus group involving psychological trauma service clinicians (n=4). RESULTS: We found that the concept of a plasticity-focused mobile phone intervention for PTSD is acceptable to patients and clinicians. Service users and clinicians both believed that the usage should be guided by a therapist, and both contributed useful inputs regarding the audiovisual aspects of the proposed serious game. It was accepted that the game would not be suitable for all patients and that clinicians would need to appropriately prescribe the usage of the game. CONCLUSIONS: The findings highlight the acceptability of the proposed serious game and clarify the user requirements for such an intervention. It is the intention of the authors to carry out a user experience evaluation using a prototype serious game in a clinical population.
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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 | 0.025 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".