Depression in Primary Care: Current and Future Challenges
Bibliographic record
Abstract
OBJECTIVES: To describe the current state of knowledge about detection and treatment of major depressive disorder (MDD) by family physicians (FPs), and to identify gaps in practice and current and future challenges. METHODS: We reviewed the recent literature on MDD (Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, or International Classification of Diseases, Revision 10) in primary care, with an emphasis on systematic reviews and meta-analyses addressing prevalence, the impact of an aging population and of chronic disease on MDD rates in primary care, detection and treatment rates by FPs, adequacy of treatment, and interventions that could improve recognition and treatment. RESULTS: About 10% of primary care patients are likely to meet criteria for MDD. The number of cases will increase as the baby boomer cohort ages and as the prevalence of chronic disease increases. The bidirectional relation between MDD and chronic disease is now firmly established. Detection and treatment rates in primary care remain low. Treatment quality is frequently inadequate in terms of follow-up and monitoring. Formal case management and collaborative care interventions are likely to provide some benefits. CONCLUSIONS: Low detection rates and low treatment rates need to be addressed. Planned reassessment may improve detection rates when the FP is uncertain whether MDD is present, but further research is needed to determine why FPs frequently do not initiate treatment, even when MDD is detected. A caring, attentive FP who monitors depressed patients is likely to have considerable placebo effect. Greater focus on integrated, concurrent treatment for MDD and chronic physical diseases in the middle-aged and elderly is also required.
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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 | 0.040 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".