Correction to: A Spanish adaptation of the Quality in Psychiatric Care—Inpatient (QPC-IP) instrument: Psychometric properties and factor structure

BMC NURSING(2022)

引用 6|浏览4
暂无评分
摘要
Background and aim Western countries share an interest in evaluating and improving quality of care in the healthcare field. The aim was to develop and examine the psychometric properties and factor structure of the Spanish version of the Quality in Psychiatric Care–Inpatient (QPC-IP) instrument. Methods A psychometric study was conducted, translating the QPC-IPS instrument into Spanish, revision of the instrument by a panel of experts, and assessing its psychometric properties. 150 psychiatric inpatients completed the QPC-IP. Test-retest reliability was assessed by re-administering the questionnaire to 75 of these patients. Results After conducting pilot testing and a cognitive interview with 30 inpatients, it was determined that the QPC-IPS was adequate and could be self-administered. A Cronbach’s alpha of 0.94 was obtained for the full instrument and values of 0.52–0.89 for the various dimensions of the questionnaire. Test re test reliability: The Intraclass Correlation Coefficient for the full questionnaire was 0.69, while for the individual dimensions values between 0.62 and 0.74 were obtained, indicating acceptable temporal stability. Convergent validity was analysed using 10-point numerical satisfaction scale, giving a positive correlation (0.49). Confirmatory factor analysis revealed six factors consistent with the original scale. The Spanish version yielded adequate results in terms of validity and reliability. Conclusion Our findings provide evidence of the convergent validity, reliability, temporal stability and construct validity of the Spanish QPC-IP for measuring patient quality in psychiatric care in Spanish hospitals. Hospital administrators can use this tool to assess and identify areas for improvement to enhance quality in psychiatric care.
更多
查看译文
关键词
Factor analysis, Inpatient psychiatric care, Nursing, Psychometric properties, Quality of care
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要