Computer-Assisted Cognitive Training for Patients with Severe Mental Illness: a Retrospective Study
Bibliographic record
Abstract
OBJECTIVES: To investigate the effectiveness of eight 45-minute sessions of computer-assisted cognitive training programme (CCTP) on improving the cognitive and functional performance of patients with Severe Mental Illness (SMI). METHODS: Medical records of 16 women and 13 men aged 26 to 62 (mean, 46.34) years who participated a CCTP were reviewed. The CCTP lasted a total of 6 hours in eight sessions over 8 weeks and comprised a series of mobile applications customised to patients' specific impaired cognitive domains. Pre- and post-test performance of cognition and functioning were assessed using the Montreal Cognitive Assessment Hong Kong version (HK-MoCA) and the Brief Assessment of Prospective Memory (BAPM), respectively. RESULTS: After the CCTP, the mean HK-MoCA score increased significantly (23.62 ± 5.34 vs 25.48 ± 3.75, d = 0.403, p = 0.001), with a significant increase in delayed recall (3.14 ± 1.75 vs 3.93 ± 1.44, d = 0.493, p = 0.003), and the mean BAPM score decreased significantly (1.44 ± 0.47 vs 1.26 ± 0.23, d = 0.486, p = 0.012). The improvement was greater in participants with primary-level education than in participants with secondary- or tertiary-level education in terms of the HK-MoCA score (3.83 ± 3.06 vs 1.35 ± 2.12, d = 0.942, p = 0.046) and the BAPM scores (-0.49 ± 0.43 vs -0.10 ± 0.29, d = 1.063, p = 0.035). CONCLUSION: Our shortened CCTP effectively enhanced the cognitive performance and daily functioning of patients with SMI. Verbal episodic memory showed the most improvement. The improvement was greater in those with primary-level education than in those with secondary- or tertiary-level education.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".