How Do Primary Care Clinicians Manage Childhood Mood and Anxiety Syndromes?
Bibliographic record
Abstract
OBJECTIVE: To describe how primary care clinicians manage children in whom they diagnose mood or anxiety syndromes. METHOD: This study is a secondary analysis of data from the multi-site Child Behavior Study (CBS)--a cross-sectional survey of primary care management of psychosocial problems. The management of children in whom clinicians identified mood or anxiety syndromes is described and compared with the management of children in whom they identified other psychosocial problems. Recruitment for the CBS occurred in 206 primary care practices in the United States, Puerto Rico, and Canada from October 1994 through June 1997. Participants were 20,861 consecutively sampled primary care attendees aged 4-15 years and 395 clinicians. Primary outcome measures for this report are rates of referral to specialized mental health care and rates of active primary care management (i.e., scheduling a follow-up appointment and/or providing ongoing counseling and/or psychotropic prescription). RESULTS: Identification of a mood or anxiety syndrome was associated with increased rates of referral to mental health compared with rates for children with other psychosocial problems. There was no effect on the proportion of children counseled during the visit. In fact, unless accompanied by a co-morbid behavioral syndrome, children receiving the diagnosis of a mood or anxiety syndrome were less likely to be offered a scheduled follow-up appointment. Rates of prescription of anti-depressants or anti-anxiety agents were higher for mood/anxiety groups but this was still uncommon (6.7%). CONCLUSIONS: Active management of childhood mood and anxiety syndromes in primary care was uncommon in the United States, Puerto Rico, and Canada in the mid-1990s.
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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 | 0.001 | 0.015 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".