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PEN-13: A New Generic 13-Item Questionnaire for Measuring Patient Enablement (German Version).

Achim SiegelAnna Tamara EhmannIngo MeyerOliver GröneWilhelm NieblingPeter MartusMonika Annemarie Rieger
Published in: International journal of environmental research and public health (2019)
Background: The purpose of our study was to develop and psychometrically test a German-language survey instrument that measures patient enablement generically and in greater detail than previous instruments. Methods: A multidisciplinary team developed 13 items to capture individual aspects of patient enablement (PEN-13). A pre-test with 26 subjects was followed by a random sample survey of N = 1168 subjects. An exploratory factor analysis was conducted in a random split-half sample of the data to explore PEN-13's factor structure; a confirmatory factor analysis was conducted in the validation sample. The internal consistency of the factors was evaluated using Cronbach's alpha, PEN-13's construct validity was checked by means of additional hypothesis testing. Results: The two factors self-management and patient-practitioner interaction, detected in the exploratory analysis, were confirmed with a few modifications in the confirmatory factor analysis, with the comparative fit index (CFI) amounting to 0.903. The Cronbach's alpha values of those two factors amounted to α = 0.90 and α = 0.82, respectively. The correlations of the PEN-13 score with the 'general self-efficacy' and 'health literacy' (HLS-EU-Q16) scores further confirmed its construct validity; the respective correlation coefficients amounted to 0.57 and 0.60. Conclusion: The German version of the survey instrument Patient Enablement Scale-13 items (PEN-13) shows acceptable psychometric properties. Practical implications: PEN-13 seems particularly suitable for health services research purposes. We recommend checking the results in another sample as well as evaluating its responsiveness to enablement-enhancing interventions.
Keyphrases
  • psychometric properties
  • case report
  • cross sectional
  • healthcare
  • palliative care
  • big data
  • social media
  • deep learning