Brief interventions for depression in primary care: a systematic review.
Bibliographic record
Abstract
OBJECTIVE: To assess existing, brief nonpharmacologic interventions that are available for primary care physicians with minimal training in psychotherapy to use in managing depression in adult patients. DATA SOURCES: MEDLINE was searched from 1996 to 2007, EMBASE was searched from 1980 to 2007, and EBM Reviews was searched from 1999 to 2007. STUDY SELECTION: Several randomized controlled trials were selected using specified criteria. Selected articles were subsequently appraised and qualitatively analyzed. SYNTHESIS: Significant improvements on depression scales were found in 6 out of 8 studies (P < .05) using various brief interventions and formal control groups. Successful interventions included bibliotherapy, websites based on cognitive-behavioural therapy (CBT), and CBT-based computer programs. Completion rates were highest when interventions were shorter, more structured, and included frequent contact or reminders from study staff. Validity limitations included small sample sizes, non-blinding of studies, and an uncertain degree of generalizability. CONCLUSION: Bibliotherapy, CBT-based websites, and CBT-based computer programs might be effective in assisting primary care physicians who have minimal training in psychotherapy in treating adult patients with depression. Health care personnel contact with patients undergoing these interventions might result in increased effectiveness. Future research is warranted in this area, and despite several limitations, findings from this study could help guide efforts in the development and evaluation of such research.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".