Collaborative Care for Psychiatric Disorders in Older Adults: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: To evaluate the mode of implementation, clinical outcomes, cost-effectiveness, and the factors influencing uptake and sustainability of collaborative care for psychiatric disorders in older adults. DESIGN: Systematic review. SETTING: Primary care, home health care, seniors' residence, medical inpatient and outpatient. PARTICIPANTS: Studies with a mean sample age of 60 years and older. INTERVENTION: Collaborative care for psychiatric disorders. METHODS: PubMed, MEDLINE, Embase, and Cochrane databases were searched up until October 2016. Individual randomized controlled trials and cohort, case-control, and health service evaluation studies were selected, and relevant data were extracted for qualitative synthesis. RESULTS: Of the 552 records identified, 53 records (from 29 studies) were included. Very few studies evaluated psychiatric disorders other than depression. The mode of implementation differed based on the setting, with beneficial use of telemedicine. Clinical outcomes for depression were significantly better compared with usual care across settings. In depression, there is some evidence for cost-effectiveness. There is limited evidence for improved dementia care and outcomes using collaborative care. There is a lack of evidence for benefit in disorders other than depression or in settings such as home health care and general acute inpatients. Attitudes and skill of primary care staff, availability of resources, and organizational support are some of the factors influencing uptake and implementation. CONCLUSIONS: Collaborative care for depressive disorders is feasible and beneficial among older adults in diverse settings. There is a paucity of studies on collaborative care in conditions other than depression or in settings other than primary care, indicating the need for further evaluation.
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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 | 0.010 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".