BACKGROUND: In primary care the General Health Questionnaire (GHQ) is used to provide an independent assessment of probable caseness of psychological disorder against which to test the ability of the general practitioner (GP) to recognize patients with current emotional problems. METHOD: The aim of the present study was to identify those clinical and psychosocial data on patients that increase the likelihood of GPs' attribution of emotional distress (GP model) and those that predict patients' emotional distress as defined by the GHQ-12 (GHQ model). The associations were explored using a classification tree technique (CHAID) and compared using bivariate logistic regression. Six GPs and 444 primary care patients took part. RESULTS: The accuracy indices of the hierarchical GP and GHQ models were 72% and 69% respectively. The availability of information on patients' psychopharmacological and psychiatric/psychological treatment in the last year was the most important predictor of attribution. Occupational, financial and housing problems and life events of loss were the most important predictors of the GHQ-12 case definition. The overall accuracy of the bivariate model was 73%. Compared with the GHQ-12, GPs gave significantly more importance to psychiatric treatment, psychopharmacological drug use and chronic illness. CONCLUSIONS: The findings suggest that to improve the detection of current emotional distress in primary care patients GPs should pay foremost and systematic attention to social problems and recent life events of loss. These problems are important clues for the possible presence of emotional distress, whereas critical patient data, in particular psychiatric history and psychopharmacological treatment, increase the probability of attribution errors.

Decisional strategies for the attribution of emotional distress in primary care

MAZZI, Maria Angela;DEL PICCOLO, Lidia;ZIMMERMANN, Christa
2004-01-01

Abstract

BACKGROUND: In primary care the General Health Questionnaire (GHQ) is used to provide an independent assessment of probable caseness of psychological disorder against which to test the ability of the general practitioner (GP) to recognize patients with current emotional problems. METHOD: The aim of the present study was to identify those clinical and psychosocial data on patients that increase the likelihood of GPs' attribution of emotional distress (GP model) and those that predict patients' emotional distress as defined by the GHQ-12 (GHQ model). The associations were explored using a classification tree technique (CHAID) and compared using bivariate logistic regression. Six GPs and 444 primary care patients took part. RESULTS: The accuracy indices of the hierarchical GP and GHQ models were 72% and 69% respectively. The availability of information on patients' psychopharmacological and psychiatric/psychological treatment in the last year was the most important predictor of attribution. Occupational, financial and housing problems and life events of loss were the most important predictors of the GHQ-12 case definition. The overall accuracy of the bivariate model was 73%. Compared with the GHQ-12, GPs gave significantly more importance to psychiatric treatment, psychopharmacological drug use and chronic illness. CONCLUSIONS: The findings suggest that to improve the detection of current emotional distress in primary care patients GPs should pay foremost and systematic attention to social problems and recent life events of loss. These problems are important clues for the possible presence of emotional distress, whereas critical patient data, in particular psychiatric history and psychopharmacological treatment, increase the probability of attribution errors.
GHQ; Primary Care Settings; emotional distress
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11562/303984
Citazioni
  • ???jsp.display-item.citation.pmc??? 2
  • Scopus 19
  • ???jsp.display-item.citation.isi??? 16
social impact