Developing immersive videos to train social cognition in individuals with schizophrenia in forensic psychiatry
Bibliographic record
Abstract
Purpose This study aims to develop immersive scenarios (immersive videos) to foster generalization of learning while addressing social cognition, a factor associated to violence in schizophrenia. The authors sought to develop immersive videos that generate a sense of presence; are socially realistic; and can be misinterpreted and, if so, lead to anger. Design/methodology/approach A multiphase mixed method was used to develop and validate the immersive scenarios. The development phase consisted of preliminary interviews and co-design workshops with patients (n = 7) and mental health practitioners (n = 7). The validation phase was conducted with patients (n = 7) and individuals without mental disorders (n = 7). Findings The development phase led to the creation of five scenarios (S1, S2, S3, S4, S5); they included social cues which could lead to self-referential and intentional biases. Results of the validation phase showed that all scenarios generated a sense of presence and were considered highly realistic. Three scenarios elicited biases and, consequently, moderate levels of anger (annoyance). Practical implications Immersive videos represent a relevant and accessible technological solution to address social-cognitive domains such as self-reference bias. Originality/value No intervention using immersive technologies had been developed or studied yet for individuals with schizophrenia at risk of violence in secure settings. This project demonstrated the feasibility of creating immersive videos which have relevant attributes to foster generalization of learning in the remediation of social-cognitive deficits.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".