Effective Components of Collaborative Care for Depression in Primary Care
Bibliographic record
Abstract
Importance: Collaborative care is a multicomponent intervention for patients with chronic disease in primary care. Previous meta-analyses have proven the effectiveness of collaborative care for depression; however, individual participant data (IPD) are needed to identify which components of the intervention are the principal drivers of this effect. Objective: To assess which components of collaborative care are the biggest drivers of its effectiveness in reducing symptoms of depression in primary care. Data Sources: Data were obtained from MEDLINE, Embase, Cochrane Library, PubMed, and PsycInfo as well as references of relevant systematic reviews. Searches were conducted in December 2023, and eligible data were collected until March 14, 2024. Study Selection: Two reviewers assessed for eligibility. Randomized clinical trials comparing the effect of collaborative care and usual care among adult patients with depression in primary care were included. Data Extraction and Synthesis: The study was conducted according to the IPD guidance of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses reporting guideline. IPD were collected for demographic characteristics and depression outcomes measured at baseline and follow-ups from the authors of all eligible trials. Using IPD, linear mixed models with random nested effects were calculated. Main Outcomes and Measures: Continuous measure of depression severity was assessed via validated self-report instruments at 4 to 6 months and was standardized using the instrument's cutoff value for mild depression. Results: A total of 35 datasets with 38 comparisons were analyzed (N = 20 046 participants [57.3% of all eligible, with minimal differences in baseline characteristics compared with nonretrieved data]; 13 709 [68.4%] female; mean [SD] age, 50.8 [16.5] years). A significant interaction effect with the largest effect size was found between the depression outcome and the collaborative care component therapeutic treatment strategy (-0.07; P < .001). This indicates that this component, including its key elements manual-based psychotherapy and family involvement, was the most effective component of the intervention. Significant interactions were found for all other components, but with smaller effect sizes. Conclusions and Relevance: Components of collaborative care most associated with improved effectiveness in reducing depressive symptoms were identified. To optimize treatment effectiveness and resource allocation, a therapeutic treatment strategy, such as manual-based psychotherapy or family integration, may be prioritized when implementing a collaborative care intervention.
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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 | 0.030 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".