Efficacy and Efficiency of Computer-Assisted Cognitive Stimulation in the Rehabilitation of Patients with Psychiatric Disorders: A Retrospective Analysis of Training Strategies and Intervention Duration
Bibliographic record
Abstract
In recent years, the integration of technology into mental health interventions has shown promising results. This retrospective study analyses the efficacy and efficiency of computer-assisted cognitive stimulation (CACS) among a group of 92 psychiatric patients, of whom 32 were randomly selected to benefit from a personalised intervention. Data were collected on demographic variables (age, education level, urban/rural area), psychiatric diagnoses, therapeutic regimens, and scores on validated psychometric scales, Mini Mental State Examination (MMSE), and Montreal Cognitive Assessment (MoCA), assessed before and after the intervention. Detailed metrics related to the training modules, such as the number of sessions, level achieved and duration of each session were also analysed. The results indicate statistically significant increases in MMSE (from 22.94 to 24.31, p < 0.001) and MoCA (from 22.06 to 24.22, p < 0.001) scores after CACS therapy, highlighting notable improvements in cognitive functions. The robust correlations between changes in scores suggest a positive impact of the intervention on cognitive performance. However, the retrospective nature of the study imposes certain limitations such as the potential influence of confounding variables, the lack of rigorous control of the intervention and the impossibility of establishing direct causal relationships. In addition, these results support the opportunity to integrate artificial intelligence (AI) components in the development of educational interventions in mental health, opening new directions for future research in the field.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".