Cognitive training of self-initiation of semantic encoding strategies in schizophrenia: A pilot study
Bibliographic record
Abstract
Available cognitive remediation interventions have a significant but relatively small to moderate impact on episodic memory in schizophrenia. The present study aimed to evaluate the efficacy and feasibility of a brief novel episodic memory training targeting the self-initiation of semantic encoding strategies. To select patients with such deficits, 28 participants with schizophrenia performed our Semantic Encoding Memory Task (SEMT) that provides a measure of self-initiated semantic encoding strategies. This task identified a deficit in 13 participants who were then offered two 60-minute training sessions one week apart. After the training, patients performed an alternate version of the SEMT. The CVLT-II (a standardised measure of semantic encoding strategies) and the BVMT-R (a control spatial memory task) were used to quantify memory pre- and post-training. After the training, participants were significantly better at self-initiating semantic encoding strategies in the SEMT (p = .004) and in the CVLT-II (p = .002). No significant differences were found in the BVMT-R. The current study demonstrates that a brief and specific training in memory strategies can help patients to improve a deficient memory process in schizophrenia. Future studies will need to test this intervention further using a randomised controlled trial, and to explore its functional impact.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".