Development and Psychometric Properties Evaluation of a Care Needs Questionnaire in Phase 1 Cardiac Rehabilitation for Patients with Coronary Artery Disease: CNCR-Q.
Neda SayadiJohanne AlterenEisa MohammadiKourosh ZareaPublished in: Journal of caring sciences (2021)
Introduction: Cardiovascular diseases (CVDs) are one of the most common chronic illnesses and the leading cause of mortality worldwide. This study aimed to design and assess the psychometric properties of questionnaire to examine the care needs of patients with coronary artery disease (CAD) in phase 1 of cardiac rehabilitation (CR). Methods: This sequential exploratory study used a mixed method with two phases. In the first phase, qualitative study was performed by analyzing the concept of Schwartz-Barcott-Kim hybrid model; and in the second phase, quantitative data were obtained and analyzed for the psychometric parameters of the designed tool. Results: The questionnaire for care needs was based on the indicators of measurement, which was identified in the qualitative phase of the study, as a tool with 40 items. After conducting face validity qualitatively, all tool items were considered important and were retained for the next steps. After completing the steps for determining the content validity ratio (CVR) and content validity index (CVI) of 40 items, they were preserved for decision making at a later stage. The results of exploratory factor analysis revealed four factors; the factor analysis of three items was eliminated and the final version of the questionnaire CNCR-Q (Care Needs Cardiac Rehabilitation-Questionnaire) with 37 items remained. Conclusion: The findings indicated that the questionnaire with properties, such as simple scoring, reliability and validity, is an appropriate tool for assessing care needs in Iranian patients with CAD. Moreover, the CNCR-Q is an effective instrument for assessing patient needs before discharge.
Keyphrases
- psychometric properties
- healthcare
- palliative care
- quality improvement
- cardiovascular disease
- cross sectional
- coronary artery disease
- pain management
- decision making
- affordable care act
- metabolic syndrome
- patient reported
- machine learning
- chronic pain
- cardiovascular events
- electronic health record
- big data
- cardiovascular risk factors
- high resolution
- artificial intelligence