The COVID-19 patients' educational needs assessment questionnaire (COPENAQ): development and psychometrics

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Abstract
Background: Despite the importance of assessing COVID-19 patients' educational needs, there is currently no standard tool for a comprehensive assessment of these needs. The present study was aimed at developing a questionnaire to assess the educational needs of COVID-19 patients and determining its psychometric properties. Methods: This study was conducted using an exploratory sequential mixed methods design in 3 stages. At the first stage, patients' educational needs were explained and determined using conventional content analysis so that a total of 15 COVID-19 patients were first selected using purposive sampling and then interviewed. At the second stage, the questionnaire items were developed using the qualitative findings and a review of valid sources related to the study subject. At the third stage, the psychometric properties of the questionnaire were determined using internal consistency reliability and the face, content, and construct validities. Results: The mean content validity ratio and the content validity index were obtained to be 0.94 and 0.92, respectively. The internal consistency was evaluated using Cronbach's alpha, which was measured to be 0.97. Based on the exploratory factor analysis, the questionnaire was developed with 36 items in four subscales of "disease recognition and treatment follow-up", "prevention of infection transmission", "medication regimen", and "psychological and physiological needs". The results of confirmatory factor analysis also showed appropriateness and approval of the structural model obtained from the questionnaire. Conclusion: This questionnaire was found to have the necessary psychometric criteria (validity and reliability) so that it can be applied to assess the educational needs of COVID-19 patients and provide better and more effective patient education for them.
Funding Information
  • Vice-Chancellor of Research and Technology of Urmia University of Medical Sciences (10327)

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