The Association of the Social Relationships CAP with Depression in Psychiatric In-patients: An Outcome Study
Bibliographic record
Abstract
Background: Depression is a worldwide problem but studies have shown that after patients with depressive symptoms are in remission, difficulties in social relationships may persist. There is a need for future research on the relationship between social function and depressive symptoms in order to facilitate development of new clinical interventions. \n Objectives: This study aimed to identify what factors contribute to the relationship between depressive symptoms and social relationships and what factors predict improvement in depressive symptoms during psychiatric hospitalization. \nMethods: This longitudinal cohort study was based on a secondary analysis of RAI-MH data from the Ontario Mental Health Reporting System (OMHRS). Depressive symptoms were measured with the Depressive Symptoms Rating Scale (DRS) and social relationships difficulties were evaluated with the interRAI Social Relationships CAP. The sample comprised of 125,120 patients from acute, long stay, addiction, psychiatric crisis units and forensic units. Sub-sample of patients with depressive symptoms and mood disorder was created (N = 38,823). Results presented in a descriptive analysis for both samples and bivariate and multivariate analysis for the sub-sample. Logistic regression analysis was performed to predict rates of improvement of depressive symptoms. \nResults: The study revealed that many factors predict outcome of depressive symptoms. Difficulties in social relationships, older age, multi-morbidity, functional impairments, trauma, and poor physical health predict decreased odds of improvements but longer hospital stay, individual therapy and family/couples therapy predict increased odds of improvements. Conclusions: The interRAI Social Relationships CAP provides a valuable tool to address social issues in patient care, assist clinical staff in care planning and provide mental health authorities information for policy making.
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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 | 0.001 |
| 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) | 0.002 | 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".