BACKGROUND: Previously published accounts of the evaluation of the effects of clinical supervision, a structured system to support health service staff, have been mainly contained to small scale qualitative studies. Over the past decade, the 36-item Manchester Clinical Supervision Scale (MCSS) has transformed the evaluation landscape and has been used as a quantitative outcome measure in upward of 90 licensed studies in 12 countries worldwide. The factor structure has been replicated by other researchers and the psychometric properties have been found robust. However, it had not been previously tested empirically using newly available and sophisticated statistical analyses. PURPOSE: This study tested the original factor structure and response format of the MCSS for goodness of fit to the Rasch model, using Rasch Unidimensional Measurement Model (RUMM) 2030 software, and investigated the validity of the questionnaire for both nursing and allied health (AH) staff. METHODS: A series of Rasch analyses were conducted on the seven subscales of the MCSS. The default procedure for RUMM software uses the partial credit model, which allows items to have varying numbers of response categories and does not assume the distance between response thresholds is uniform. RESULTS: Detailed Rasch analyses indicated that the 36-item version of the MCSS could be reduced to 26 items and result in improved fit statistics for six subscales rather than seven. CONCLUSIONS: This study reconfirmed the established psychometric properties of the MCSS, now renamed the MCSS-26.
BACKGROUND: Previously published accounts of the evaluation of the effects of clinical supervision, a structured system to support health service staff, have been mainly contained to small scale qualitative studies. Over the past decade, the 36-item Manchester Clinical Supervision Scale (MCSS) has transformed the evaluation landscape and has been used as a quantitative outcome measure in upward of 90 licensed studies in 12 countries worldwide. The factor structure has been replicated by other researchers and the psychometric properties have been found robust. However, it had not been previously tested empirically using newly available and sophisticated statistical analyses. PURPOSE: This study tested the original factor structure and response format of the MCSS for goodness of fit to the Rasch model, using Rasch Unidimensional Measurement Model (RUMM) 2030 software, and investigated the validity of the questionnaire for both nursing and allied health (AH) staff. METHODS: A series of Rasch analyses were conducted on the seven subscales of the MCSS. The default procedure for RUMM software uses the partial credit model, which allows items to have varying numbers of response categories and does not assume the distance between response thresholds is uniform. RESULTS: Detailed Rasch analyses indicated that the 36-item version of the MCSS could be reduced to 26 items and result in improved fit statistics for six subscales rather than seven. CONCLUSIONS: This study reconfirmed the established psychometric properties of the MCSS, now renamed the MCSS-26.
Authors: David A Snowdon; Shae Cooke; Katherine Lawler; Grant Scroggie; Kimberley Williams; Nicholas F Taylor Journal: Physiother Can Date: 2020 Impact factor: 1.037
Authors: Michael Schriver; Vincent Kalumire Cubaka; Peter Vedsted; Innocent Besigye; Per Kallestrup Journal: Glob Health Action Date: 2018 Impact factor: 2.640
Authors: David A Snowdon; Michelle Sargent; Cylie M Williams; Stephen Maloney; Kirsten Caspers; Nicholas F Taylor Journal: BMC Health Serv Res Date: 2019-12-31 Impact factor: 2.655