The Implementation and Review of Cognitive Remediation Training for First Episode Psychosis in Singapore
Bibliographic record
Abstract
Objective: Early intervention in patients with first episode psychosis (FEP) can improve cognitive abilities, with both short- and long-term benefits. In this paper, we describe the implementation and review of cognitive remediation training (CRT) in an Asian FEP population. The outcomes of the training are also evaluated and discussed. Methods: This naturalistic paper describes in detail the real-life implementation and conduct of CRT in an early psychosis intervention service. One hundred and nine patients with FEP underwent a 24-session CRT programme, using Cogpack and Neuropsychological Educational Approach to Remediation. The program is evaluated with pre- and post-CRT assessment scores which included Montreal Cognitive Assessment and Brief Assessment of Cognition in Schizophrenia. The rates of improvement on these cognitive assessments were evaluated using paired t-tests, with statistical significance set at p ≤ 0.05. Results: Of the 109 patients who underwent CRT, a total of 92 (84.4%) completed all 24 sessions. Paired t-tests between pre- and post-CRT assessments scores revealed that participants significantly improved on majority of the measures, including verbal memory, digit sequencing, and symbol coding. Conclusion: As with other cognitive remediation programmes, CRT has shown to improve cognitive functioning in patients with FEP. The results support the use of CRT in an Asian context and may serve as guidance for the implementation of similar training programmes in other Asian early psychosis intervention services.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".