Cognitive training of self-initiation of semantic encoding strategies in schizophrenia: A pilot study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 it