Daily Task Performance and Information Processing among People with Schizophrenia and Healthy Controls: A Comparative Study
Bibliographic record
Abstract
Introduction: Many individuals with schizophrenia have information processing difficulties. This study investigated the use of information processing skills during the performance of a daily task by participants with schizophrenia and compared it to that of participants without a psychiatric diagnosis. Studies comparing similar groups found differences in the number and types of errors. However, there is limited knowledge about the related problematic information processing skills. This information could help to better pinpoint the needs of this group of clients. Method: Participants were paired based on age and gender. Information processing skills were assessed with the Perceive, Recall, Plan, and Perform system of task analysis. Generalized linear mixed models were used to compare both groups. Results: Individuals with schizophrenia made more accuracy errors and had more difficulties when attending and gathering information and when planning was required during the task. They were also more cognitively impaired than the comparison group. Conclusion: The large number of accuracy errors may result from specific skills deficits that impact on other processing skills or from a general vulnerability affecting most processing skills. In future studies, the influence of employment and of the social environment of housing on task performance should be investigated.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".