A Brief Scale to Assess Hospital Doctors' Attitudes toward Collaborative Care for Mental Health
Bibliographic record
Abstract
OBJECTIVE: Collaborative care may improve mental health management in hospital settings. However, no scales assess doctors' attitudes toward its 2 core components: mental health management by nonpsychiatric physicians and psychiatric consultation. Our objective was to develop and assess the reliability and validity of the Doctors' Attitudes Toward Collaborative Care for Mental Health (DACC-MH) Scale. METHOD: Fifteen items assessing doctors' attitudes toward management of mental health problems (10 items) and psychiatric consultation (5 items) were administered to 225 physicians and surgeons from a London hospital. Item responses were dichotomous (agree or disagree). Confirmatory factor analysis models were conducted using Mplus for dichotomous data to identify items for inclusion in the DACC-MH and to test the validity of the 2 hypothesized factors. Known-groups validity was tested by comparing scores of surgeons and physicians, as physicians have been shown to view mental health management and psychiatric consultation more favourably. RESULTS: The 8-item DACC-MH included a 4-item Attitudes Toward Management of Mental Health Problems factor (Cronbach's a = 0.65) and a 4-item Attitudes Toward Psychiatric Consultation factor (alpha = 0.67; overall scale alpha = 0.70). Model fit was good (chi2 = 12.7, df = 11, P = 0.31; Comparative Fit Index = 0.99; Tucker-Lewis Index = 0.99; root mean square error of approximation = 0.03) with all factor loadings of 0.46 or greater. As hypothesized, physician scores were significantly higher than surgeon scores on both subscales, indicating more positive attitudes toward management of mental health problems and psychiatric consultation. CONCLUSIONS: Preliminary evidence was found for the validity of the DACC-MH, which will facilitate efforts to evaluate readiness of doctors to engage in collaborative 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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".