Chronic Disease Management for Depression in Primary Care: A Summary of the Current Literature and Implications for Practice
Bibliographic record
Abstract
OBJECTIVE: To review randomized controlled trials (RCTs) evaluating chronic disease management models for depression in primary care and to look at the implications for clinical practice in Canada. METHODS: We reviewed all RCTs conducted between 1992 and 2006, including other reviews and analyses of pooled data. Using various search terms, we searched PsycINFO, Cinahl (1982 to May 2005), MEDLINE (1995 to 2005), EMBASE, The Cochrane Library, and PubMed. RESULTS: There is conclusive evidence for the benefits of changing systems of care delivery to support the more effective management of depression in primary care. Most studies have demonstrated improved outcomes in terms of symptom reduction, relapse prevention, functioning in the community, adherence to treatment, community and workplace involvement, and satisfaction with care received. CONCLUSIONS: Primary care practices need to examine how they can incorporate different concepts and models for managing depression. Components to consider include case registries, care managers or coordinators, treatment algorithms, follow-up and monitoring after a treated episode, care and relapse prevention plans, visits by psychiatrists, and training and ongoing education for all providers.
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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 | 0.021 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.004 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".