Predictors of treatment utilisation at cognitive remediation groups for schizophrenia: The roles of neuropsychological, psychological and clinical variables
Bibliographic record
Abstract
The present study highlights the importance of carefully assessing neuropsychological functioning at the outset of cognitive remediation (CR) treatment. The effects of neuropsychological, psychological, and clinical variables on treatment utilisation (TU) in CR groups for individuals with schizophrenia were examined. Data included neuropsychological and psychosocial assessments conducted with 39 adult clients enrolled in CR as part of their ongoing outpatient therapy. TU was calculated using the percentage of sessions attended over a three-month period. Better global neuropsychological functioning (r = .46, p = .007), attention/working memory (r = .39, p = .03), and processing speed (r = .44, p = .01) were each associated with greater TU. Trend-level associations with TU were observed with executive functioning (r = .33, p = .06) and verbal learning (r = .23; p = .07). Higher rates of self-reported cognitive complaints were associated with lower TU (r = -.45, p = .01). Hierarchical regression analyses revealed that both objective and subjective indicators of neuropsychological functioning independently contributed to the prediction of TU. This information can serve to help providers develop empirically informed strategies to support their clients' CR treatment utilisation. The implications from these findings can be used as a way to provide ongoing guidance for service provision and can aid in improving CR treatment utilisation, and thus treatment effectiveness, in clinical settings.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".