Primary Care Treatment of Pediatric Psychosocial Problems: A Study From Pediatric Research in Office Settings and Ambulatory Sentinel Practice Network
Bibliographic record
Abstract
OBJECTIVE: Psychosocial problems cause much of the morbidity among children, and their frequency of presentation in primary care is growing. How is primary care treatment of children's psychosocial problems affected by child symptoms, physician training, practice structure, insurance, physician/patient relationship, and family demographics? DESIGN: Questionnaire study of treatment of psychosocial problems during office visits by children. SETTINGS: At total of 401 primary care offices from 44 US states, Puerto Rico, and Canada. PATIENTS: From 21 150 children seen in office visits, we selected children with an identified psychosocial problem but who were not already receiving specialty mental health services (n = 2618 children). OUTCOME MEASURES: Clinicians' decisions to counsel families, to refer children to mental health specialists, or to prescribe medication. RESULTS: The treatment choices of primary care clinicians (PCCs) were generally independent of patients' demographics or insurance status. Clinicians' training, beliefs about mental health, and practice structure had no effect on treatment choices. However, clinicians seeing their own patients were more likely to prescribe medications for attention problems. The clinician's perception about whether the parent agreed with the treatment choice was important for every treatment modality. Counseling and referral were more common and medication was less common when a problem was newly recognized at the visit. CONCLUSIONS: Structural factors such as practice type, insurance coverage, and physician training were less important for treatment than were process factors, such as whether the visit was a psychosocial problem visit, whether the problem was newly or previously recognized, and whether the family and clinician were familiar with each other and in accord about treatment.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".