Understanding Depressive Symptoms in Individuals with Schizophrenia: Analyses Using the Resident Assessment Instrument – Mental Health (RAI-MH)
Bibliographic record
Abstract
Objective: The primary aim of this study was to better understand the role of depressive and negative symptoms in patients with schizophrenia. As such, two specific research questions guide this analysis: (1) What factors are associated with depressive and negative symptoms at Time 1 across four major psychiatric diagnoses (patients with schizophrenia, mood disorder, both schizophrenia and mood disorders, and patients whose primary diagnosis is neither schizophrenia nor mood disorder)? (2) To what extent do depressive and negative symptoms improve over time among individuals with schizophrenia? More specifically, what variables predict an improvement in these symptoms? \nMethods: The study involved analysis of secondary data from 3269 in-patients from 15 psychiatric facilities in the Province of Ontario, Canada. Patients were assessed using the Resident Assessment Instrument – Mental Health (RAI-MH). Bivariate analyses were performed examining demographic, clinical, social, and other factors as independent variables and depressive and negative symptom scores among each of the four diagnostic groups: schizophrenia, mood disorder, both schizophrenia and mood disorder, and neither schizophrenia nor mood disorder. Logistic regression of depressive and negative symptoms, as dependent variables, were performed on demographic, psychiatric, clinical, social, and other variables, as the independent variables. \nResults: Variables associated with depressive and negative symptoms did not necessarily predict an improvement of depressive and negative symptoms over time. Findings from logistic regression models showed that statistically significant predictors of improvement in depressive and negative symptoms included the following variables: (1) not having a diagnosis of schizophrenia; (2) insight into one’s condition; (3) fewer number of recent psychiatric admissions (over the last two year period); and (5) being administered both atypical and typical antipsychotic medications. \nConclusions: Depressive and negative symptoms are prevalent in schizophrenia and are associated with demographic, psychiatric, and social variables. Depressive and negative symptoms do not share the same pattern across diagnoses, suggesting that these symptoms represent a unique profile within each diagnostic group. Moreover, both atypical and typical antipsychotic medications, in combination, were shown to be more effective at treating depressive and negative symptoms than either typical or atypical medications alone.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".