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Developing and validating a tool for assessing the confidence in the competence of midwifery tutors in India on WHO core competency domains.

Paridhi JhaBharati SharmaPrabhu PonnusamyPurna Chandra SahooVikas Kumar JhaNishtha KathuriaDevika MehraSunanda GuptaArvind PandeyRam ChaharFrances Emma McConvilleMedha GandhiMalin Bogren
Published in: PLOS global public health (2024)
Negligible quantitative research evidence exists on standardisation and psychometric validation of questionnaires that measure midwifery educators' confidence in their competence. This study developed a self-assessment of confidence in competence questionnaire in India based on the WHO Midwifery Educator Core Competencies (2014) with an aim to develop and validate a self-assessment tool measuring midwifery tutors' confidence in competence in imparting quality midwifery education. The questionnaire was developed as part of a multi-centre study to identify confident midwifery tutors for further training as educators, supporting India's rollout of professional midwives. The questionnaire underwent rigorous psychometric testing among 2016 midwifery tutors in India. Following exploratory Principal Component Analyses (PCA), the nine core competencies outlined in the WHO document were analysed separately. The results indicate that the questionnaire is psychometrically valid, with an internal consistency range of 0.81-0.93 for the nine domains. This robust testing process ensures the reliability and validity of the questionnaire. The self-assessment questionnaire can potentially be a valuable tool in India and other high-, middle-, and low-income countries. From a programmatic perspective, it can help identify key gaps and prioritise training needs, particularly in low-resource settings, so that limited resources are best utilised to fill the most prominent gaps. Furthermore, it can provide a universal platform for comparing data from different settings, facilitating global collaboration and learning in midwifery education.
Keyphrases
  • psychometric properties
  • cross sectional
  • healthcare
  • patient reported
  • mass spectrometry
  • global health
  • electronic health record
  • nursing students
  • data analysis