Prospective memory in schizophrenia: Relationship to medication management skills, neurocognition, and symptoms in individuals with schizophrenia.
Bibliographic record
Abstract
OBJECTIVE: Impaired adherence to medication regimens is a serious concern for individuals with schizophrenia linked to relapse and poorer outcomes. One possible reason for poor adherence to medication is poor ability to remember future intentions, labeled prospective memory skills. It has been demonstrated in several studies that individuals with schizophrenia have impairments in prospective memory that are linked to everyday life skills. However, there have been no studies, to our knowledge, examining the relationship of a clinical measure of prospective memory to medication management skills, a key element of successful adherence. METHODS: In this Study 41 individuals with schizophrenia and 25 healthy adults were administered a standardized test battery that included measures of prospective memory, medication management skills, neurocognition, and symptoms. RESULTS: Individuals with schizophrenia demonstrated impairments in prospective memory (both time and event-based) relative to healthy controls. Performance on the test of prospective memory was correlated with the standardized measure of medication management in individuals with schizophrenia. Moreover, the test of prospective memory predicted skills in medication adherence even after measures of neurocognition were accounted for. CONCLUSIONS: This suggests that prospective memory may play a key role in medication management skills and thus should be a target of cognitive remediation programs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".