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Research ArticleOriginal Article
Open Access

Quality of psychiatric referrals in Saudi Arabia. A structural equation modeling approach

Naseem A. Qureshi, Henk G. Schmidt, Henk T. Van der Molen, Tariq A. Al-Habeeb and Mohi E. Magzoub
Neurosciences Journal January 2007, 12 (1) 53-61;
Naseem A. Qureshi
Administration for Mental Health & Social Services, Ministry of Health, Riyadh 11176, Kingdom of Saudi Arabia. Tel: +966 (1) 4738269. Fax. +966 (1) 2918017. E-mail: [email protected]
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Henk G. Schmidt
Administration for Mental Health & Social Services, Ministry of Health, Riyadh 11176, Kingdom of Saudi Arabia. Tel: +966 (1) 4738269. Fax. +966 (1) 2918017. E-mail: [email protected]
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Henk T. Van der Molen
Administration for Mental Health & Social Services, Ministry of Health, Riyadh 11176, Kingdom of Saudi Arabia. Tel: +966 (1) 4738269. Fax. +966 (1) 2918017. E-mail: [email protected]
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Tariq A. Al-Habeeb
Administration for Mental Health & Social Services, Ministry of Health, Riyadh 11176, Kingdom of Saudi Arabia. Tel: +966 (1) 4738269. Fax. +966 (1) 2918017. E-mail: [email protected]
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Mohi E. Magzoub
Administration for Mental Health & Social Services, Ministry of Health, Riyadh 11176, Kingdom of Saudi Arabia. Tel: +966 (1) 4738269. Fax. +966 (1) 2918017. E-mail: [email protected]
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Abstract

OBJECTIVE: This study seeks to model proposed causal relationships between the quality of psychiatric referral letters, and its indicators, linked to the features of the referred patient, referring physician, and practice setting.

METHODS: This study was executed at Buraidah Mental Health Hospital, Saudi Arabia, in the year 2000-2002. Data regarding 18 independent variables underlying 3 latent constructs and one dependent variable represented by quality of psychiatric referral letter score (outcome) was derived from patient files, physician training records, and 540 psychiatric referrals. Structural equation modeling was used to analyze the data for examining proposed causal relationships between the quality of psychiatric referral letters, and its potential predictors.

RESULTS: The structural equation modeling analysis revealed a reasonably good fit of the proposed model to the data based on various fit indices. The tested model explained 67% of the variance in the quality of psychiatric referral letters. The referring physician characteristics (experience, education, and psychiatric training) and features of the referral setting (nature of setting and referral letters-administrative information) were highly significant indicators of quality of psychiatric referral letters, which in turn was negatively predicted by patient features including severity of the mental illness.

CONCLUSION: Despite some caveats, the quality of psychiatric referral letters is accurately predicted by 3 latent constructs represented by referring physician skills, nature of the setting, and patient socio-clinical features.

  • Copyright: © Neurosciences

Neurosciences is an Open Access journal and articles published are distributed under the terms of the Creative Commons Attribution-NonCommercial License (CC BY-NC). Readers may copy, distribute, and display the work for non-commercial purposes with the proper citation of the original work.

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Neurosciences Journal: 12 (1)
Neurosciences Journal
Vol. 12, Issue 1
1 Jan 2007
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Quality of psychiatric referrals in Saudi Arabia. A structural equation modeling approach
Naseem A. Qureshi, Henk G. Schmidt, Henk T. Van der Molen, Tariq A. Al-Habeeb, Mohi E. Magzoub
Neurosciences Journal Jan 2007, 12 (1) 53-61;

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Quality of psychiatric referrals in Saudi Arabia. A structural equation modeling approach
Naseem A. Qureshi, Henk G. Schmidt, Henk T. Van der Molen, Tariq A. Al-Habeeb, Mohi E. Magzoub
Neurosciences Journal Jan 2007, 12 (1) 53-61;
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© 2025 Neurosciences Journal Neurosciences is copyright under the Berne Convention and the International Copyright Convention. All rights reserved. Neurosciences is an Open Access journal and articles published are distributed under the terms of the Creative Commons Attribution-NonCommercial License (CC BY-NC). Readers may copy, distribute, and display the work for non-commercial purposes with the proper citation of the original work. Electronic ISSN 1658-3183. Print ISSN 1319-6138.

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