Motivation and engagement during cognitive training for schizophrenia spectrum disorders
Bibliographic record
Abstract
Motivation and engagement are important factors associated with therapeutic outcomes in cognitive training for schizophrenia. The goals of the present report were to examine relations between objective treatment engagement (number of sessions attended, amount of homework completed) and self-reported motivation (intrinsic motivation and perceived competence to complete cognitive training) with neurocognitive and functional outcomes from cognitive training. Data from a clinical trial comparing two cognitive training approaches in schizophrenia-spectrum disorders were utilized in the current report (n = 38). Relations were examined between baseline intrinsic motivation, perceived competence, homework completion, and session attendance with improvements in neurocognition, functional competence, and community functioning. Number of sessions attended (r = 0.38) and time doing homework (r = 0.51) were significantly associated with improvements in neurocognition. Homework completion was associated with change in community functioning at a trend-level (r = 0.30). Older age was associated with greater treatment engagement (β = 0.37) and male biological sex was associated with greater self-reported motivation (β = 0.43). Homework completion significantly mediated the relationship between session attendance and neurocognitive treatment outcomes. Objective measures of treatment engagement were better predictors of treatment outcomes than subjective measures of motivation. Homework completion was most strongly related to treatment outcomes and mediated the relationship between session attendance and treatment outcomes, suggesting continued engagement with cognitive stimulation may be an especially important component of cognitive remediation programs. Future research should examine methods to improve homework completion and session attendance to maximize therapeutic outcomes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".