Factors Impacting Access and Engagement of Cognitive Remediation Therapy for People with Schizophrenia: A Systematic Review
Bibliographic record
Abstract
Objectives Neurocognitive deficits are central in schizophrenia. Cognitive remediation has proven effective in alleviating these deficits, with medium effect sizes. However, sizeable attrition rates are reported, with the reasons still uncertain. Furthermore, cognitive remediation is not part of routine mental health care. We conducted a systematic review to investigate factors that influence access and engagement of cognitive remediation in schizophrenia. Methods We systematically searched the PubMed, Web of Science, and PsycINFO databases for peer-reviewed articles including a cognitive remediation arm, access, and engagement data, and participants with schizophrenia spectrum disorders aged 17–65 years old. Duplicates and studies without a distinct cognitive remediation component, protocol papers, single case studies, case series, and reviews/meta-analyses were excluded. Results We included 67 studies that reported data on access and engagement, and extracted quantitative and qualitative data. Access data were limited, with most interventions delivered on-site, to outpatients, and in middle- to high-income countries. We found a median dropout rate of 14.29%. Only a small number of studies explored differences between dropouts and completers ( n = 5), and engagement factors ( n = 13). Dropouts had higher negative symptomatology and baseline self-efficacy, and lower baseline neurocognitive functioning and intrinsic motivation compared to completers. The engagement was positively associated with intrinsic motivation, self-efficacy, perceived usefulness, educational level, premorbid intelligence quotient, baseline neurocognitive functioning, some neurocognitive outcomes, and therapeutic alliance; and negatively associated with subjective cognitive complaints. Qualitative results showed good acceptability of cognitive remediation, with some areas for improvement. Conclusions Overall, access and engagement results are scarce and heterogeneous. Further investigations of cognitive remediation for inpatients, as well as remote delivery, are needed. Future clinical trials should systematically explore attrition and related factors. Determining influential factors of access and engagement will help improve the implementation and efficacy of cognitive remediation, and thus the recovery of people with schizophrenia.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".