Patterns and Correlates of Contacting Clergy for Mental Disorders in the United States
Bibliographic record
Abstract
OBJECTIVE: To present nationally representative data on the part played by clergy in providing treatment to people with mental disorders in the United States. DATA SOURCES: The National Comorbidity Survey (NCS), a nationally representative general population survey of 8,098 respondents ages 15-54. STUDY DESIGN: Cross-sectional survey. DATA COLLECTION: A modified version of the Composite International Diagnostic Interview was used to assess DSM-III-R mental disorders. Reports were obtained on age of onset of disorders, age of first seeking treatment, and treatment in the 12 months before interview with each of six types of professionals (clergy, general medical physicians, psychiatrists, other mental health specialists, human services providers, and alternative treatment providers). PRINCIPAL FINDINGS: One-quarter of those who ever sought treatment for mental disorders did so from a clergy member. Although there has been a decline in this proportion between the 1950s (31.3 percent) and the early 1990s (23.5 percent), the clergy continue to be contacted by higher proportions than psychiatrists (16.7 percent) or general medical doctors (16.7 percent). Nearly one-quarter of those seeking help from clergy in a given year have the most seriously impairing mental disorders. The majority of these people are seen exclusively by the clergy, and not by a physician or mental health professional. CONCLUSIONS: The clergy continue to play a crucial role in the U.S. mental health care delivery system. However, interventions appear to be needed to ensure that clergy members recognize the presence and severity of disorders, deliver therapies of sufficient intensity and quality, and collaborate appropriately with health care professionals.
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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 | 0.004 |
| 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 | 0.000 |
| 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".