Strategies for Achieving Better Cognitive Health in Individuals with Schizophrenia Spectrum: A Focus on the Canadian Landscape: Stratégies pour atteindre une meilleure santé cognitive chez les personnes souffrant du spectre de la schizophrénie : un regard sur le paysage canadien
Bibliographic record
Abstract
BACKGROUND: Schizophrenia spectrum disorders (SSDs) are a group of psychiatric disorders characterized by positive and negative symptoms as well as cognitive impairment that can significantly affect daily functioning. METHOD: We reviewed evidence-based strategies for improving cognitive function in patients with SSDs, focusing on the Canadian landscape. RESULTS: Although antipsychotic medications can address the positive symptoms of SSDs, cognitive symptoms often persist, causing functional impairment and reduced quality of life. Moreover, cognitive function in patients with SSDs is infrequently assessed in clinical practice, and evidence-based recommendations for addressing cognitive impairment in people living with schizophrenia are limited. While cognitive remediation (CR) can improve several domains of cognitive function, most individuals with SSDs are currently not offered such an intervention. While the development of implementation strategies for CR is underway, available and emerging pharmacological treatments may help overcome the limited capacity for psychosocial approaches. Furthermore, combining pharmacological with non-pharmacological interventions may improve outcomes compared to pharmacotherapy or CR alone. CONCLUSION: This review highlights the challenges and discusses the potential solutions related to the assessment and management of cognitive impairment to help mental health-care practitioners better manage cognitive impairment and improve daily functioning in individuals with SSDs. PLAIN LANGUAGE SUMMARY TITLE: Improving Thinking Skills in People With Schizophrenia: A Focus on Canada.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".