Expert Survey for the Management of Adolescent Depression in Primary Care
Bibliographic record
Abstract
OBJECTIVE: Primary care clinics have become the "de facto" mental health clinics for teens with mental health problems such as depression; however, there is little guidance for primary care professionals who are faced with treating this population. This study surveyed experts on key management issues regarding adolescent depression in primary care where empirical literature was scant or absent. METHODS: Participants included experts from family medicine, pediatrics, nursing, psychology, and child psychiatry, identified through nonprobability sampling. The expert survey was developed on the basis of information from focus groups with patients, families, and professionals and from the research literature and included sections on early identification, assessment and diagnosis, initial management, treatment, and ongoing management. Means, standard deviations, and confidence intervals were calculated for each survey item. RESULTS: Seventy-eight of 81 experts agreed to participate (return rate of 96%). Fifty-three percent of the experts (n = 40) were primary care professionals. Experts endorsed routine surveillance for youth at high risk for depression, as well as the use of standardized measures as diagnostic aids. For treatment, "active monitoring" was deemed appropriate in mild depression with recent onset. Medication and psychotherapy were considered acceptable options for treatment of moderate depression without complicating factors such as comorbid illness. Fluoxetine was rated as the most appropriate antidepressant for use in this population. Finally, experts agreed that patients who are started on antidepressants should be followed within 2 weeks after initiation. CONCLUSIONS: Survey results support the identification and management of adolescent depression in the primary care setting and, in specific situations, referral and co-management with specialty mental health professionals. Even with the recent controversies around treatment, experts across primary care and specialty mental health alike agreed that active monitoring, pharmacotherapy with selective serotonin reuptake inhibitors, and psychotherapy can be appropriate under certain clinical circumstances when initiated within primary care settings.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".