INTEGRATING DEPRESSION SCREENING INTO FAMILY MEDICINE: STRATEGIES FOR EARLY DETECTION AND TIMELY INTERVENTION
Bibliographic record
Abstract
Background: Depression is frequently underdiagnosed in family medicine, despite its high prevalence and severe consequences. This systematic review explores evidence-based strategies for enhancing depression screening and early intervention within primary care contexts. Methods: Following PRISMA 2020 guidelines, a comprehensive literature search was conducted across PubMed, Scopus, Web of Science, CINAHL, and Google Scholar. Eligible studies included adults (≥18 years), utilized structured depression screening interventions in family medicine, and reported outcomes such as detection rates, treatment initiation, and symptom reduction. Data extraction, quality appraisal, and narrative synthesis were performed. Results: Fifteen studies (including RCTs, systematic reviews, and cohort studies) met the inclusion criteria. Interventions integrating screening with structured follow-up, nurse-led assessments, or digital tools (e.g., AI screening) significantly improved detection (15–35%) and treatment initiation rates (up to 42%). Tailored protocols for specific populations, such as postpartum women and adolescents, showed increased efficacy. Multifaceted interventions were more effective than screening alone. Conclusion: Effective depression screening in family medicine requires integrated, context-aware strategies that combine screening tools with system-level supports. Training, digital solutions, and workflow redesign enhance feasibility and patient outcomes. Future implementation must address structural barriers and inequities in access to ensure scalable, high-impact mental health care.
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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 | 0.045 | 0.105 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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".