Literature DB >> 19659489

A multi-method analysis of free-text comments from the UK General Medical Council Colleague Questionnaires.

Suzanne H Richards1, John L Campbell, Emily Walshaw, Andy Dickens, Michael Greco.   

Abstract

CONTEXT: Colleague surveys are important sources of information on a doctor's professional performance in UK revalidation plans. Colleague surveys are analysed by deriving quantitative measures from rating scales. As free-text comments are also recorded, we explored the utility of a mixed-methods approach to their analysis.
METHODS: A volunteer sample of practising UK doctors (from acute, primary and other care settings) undertook a General Medical Council (GMC) colleague survey. Up to 20 colleagues per doctor completed an online Colleague Questionnaire (CQ), which included 18 performance evaluation items and an optional comment box. The polarity of each comment was noted and a qualitative content analysis undertaken. Emerging themes were mapped onto existing items to identify areas not previously captured. We then quantitatively analysed the associations between the polarity of comments (positive/adverse) and their related item scale scores.
RESULTS: A total of 1636 of 4269 (38.3%) colleagues recorded free-text comments (median = 14 per doctor) and most were unequivocally positive; only 127 of 1636 (7.8%) recorded negative statements and these were clustered on a subset comprising 80 of 302 (26.5%) doctors. Doctors' overall mean CQ performance scores were significantly correlated with the numbers of colleagues recording positive (r = 0.35; P < 0.0001) and adverse (r = - 0.40; P = 0.0003) comments. In total, 1224 of 1636 (74.8%) comments included statements that mapped on CQ items, and statistically significant associations (P < 0.05) were observed for 14 of 15 items. Five global themes (innovativeness, interpersonal skills, popularity, professionalism, respect) were identified in 904 of 1636 (73.9%) comments.
CONCLUSIONS: There is an inevitable trade-off between the capturing of indicators of problematic performance (i.e. adverse statements which contradict a positive scale rating) and the ease with which such statements can be identified. Our data suggest there is little benefit in routinely analysing narrative comments for the purposes of revalidation.

Mesh:

Year:  2009        PMID: 19659489     DOI: 10.1111/j.1365-2923.2009.03416.x

Source DB:  PubMed          Journal:  Med Educ        ISSN: 0308-0110            Impact factor:   6.251


  11 in total

Review 1.  Workplace-based Assessment; Applications and Educational Impact.

Authors:  Salman Yousuf Guraya
Journal:  Malays J Med Sci       Date:  2015-11

2.  Clinical Instructors' Perceptions of Internationally Educated Physical Therapists' Readiness to Practise during Supervised Clinical Internships in a Bridging Programme.

Authors:  Michael E Kalu; Sharon Switzer-Mclntrye; Martine Quesnel; Catherine Donnelly; Kathleen E Norman
Journal:  Physiother Can       Date:  2021       Impact factor: 1.037

3.  Web-based textual analysis of free-text patient experience comments from a survey in primary care.

Authors:  Inocencio Daniel Maramba; Antoinette Davey; Marc N Elliott; Martin Roberts; Martin Roland; Finlay Brown; Jenni Burt; Olga Boiko; John Campbell
Journal:  JMIR Med Inform       Date:  2015-05-06

4.  Does what we write matter? Determining the features of high- and low-quality summative written comments of students on the internal medicine clerkship using pile-sort and consensus analysis: a mixed-methods study.

Authors:  Lauren Gulbas; William Guerin; Hilary F Ryder
Journal:  BMC Med Educ       Date:  2016-05-13       Impact factor: 2.463

5.  Supervised Machine Learning Algorithms Can Classify Open-Text Feedback of Doctor Performance With Human-Level Accuracy.

Authors:  Chris Gibbons; Suzanne Richards; Jose Maria Valderas; John Campbell
Journal:  J Med Internet Res       Date:  2017-03-15       Impact factor: 5.428

6.  Competencies and Feedback on Internal Medicine Residents' End-of-Rotation Assessments Over Time: Qualitative and Quantitative Analyses.

Authors:  Ara Tekian; Yoon Soo Park; Sarette Tilton; Patrick F Prunty; Eric Abasolo; Fred Zar; David A Cook
Journal:  Acad Med       Date:  2019-12       Impact factor: 6.893

7.  Qualitative analysis of patients' feedback from a PROMs survey of cancer patients in England.

Authors:  Jessica Corner; Richard Wagland; Adam Glaser; Sir Mike Richards
Journal:  BMJ Open       Date:  2013-04-10       Impact factor: 2.692

8.  Views and experiences of seeking information and help for vitiligo: a qualitative study of written accounts.

Authors:  Emma Teasdale; Ingrid Muller; Amirah Abdullah Sani; Kim S Thomas; Beth Stuart; Miriam Santer
Journal:  BMJ Open       Date:  2018-01-11       Impact factor: 2.692

9.  Gaining an accurate reflection of the reality of palliative care through the use of free-text feedback in questionnaires: the AFTER study.

Authors:  Anna Victoria Bowyer; Ilora Finlay; Jessica Baillie; Anthony Byrne; Jacqui McCarthy; Catherine Sampson; Veronica Snow; Annmarie Nelson
Journal:  BMJ Support Palliat Care       Date:  2016-02-17       Impact factor: 3.568

10.  Impact of COVID-19 on Immunization Services for Maternal and Infant Vaccines: Results of a Survey Conducted by Imprint-The Immunising Pregnant Women and Infants Network.

Authors:  Anja Saso; Helen Skirrow; Beate Kampmann
Journal:  Vaccines (Basel)       Date:  2020-09-22
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.