Effect of non‐pharmacological interventions on source memory processes in the early course of psychosis: A systematic review
Bibliographic record
Abstract
People with a psychotic disorder suffer from major cognitive impairments which prevent their functional recovery. Source memory impairments have been shown to be associated with psychotic symptoms and even to precede their onset. Source memory has thus been hypothesized as a cognitive precursor of psychosis. However, few interventions targeting source memory are included in current therapeutic approaches for early psychosis. AIM: This systematic review aimed to identify non-pharmacological interventions for early psychosis which have impacted source memory processes. METHODS: Studies were selected from nine databases when they included: (a) a non-pharmacological intervention involving a sample of patients with early-onset psychotic disorder or subclinical psychotic symptoms; and (b) effects on source memory processes, measured directly or inferred through an episodic memory task. RESULTS: Thirteen studies were identified, including two cognitive remediation programs and one repetitive transcranial magnetic stimulation treatment that reported beneficial effects on source memory. CONCLUSIONS: Relevant intervention strategies for source memory impairments were identified. This review points up a need to further develop interventions targeting theoretically defined source memory concepts and assess their effects with specific and valid tasks. Recommendations regarding underlying mechanisms which could have a beneficial impact on source memory may provide guidance for the future development of early psychosis interventions.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".