Saskia C Sanderson1, Bao Sheng Loe2, Maddie Freeman3, Camila Gabriel4, Danielle C Stevenson5, Chris Gibbons6, Lyn Chitty7, Celine Lewis7. 1. North East Thames Regional Genetics Services, Great Ormond Street Hospital, London, UK; UCL Great Ormond Street Institute of Child Health, UK; Department of Behavioural Science and Health, University College London, London, UK. Electronic address: saskia.sanderson@ucl.ac.uk. 2. The Psychometrics Centre, University of Cambridge, Cambridge, UK. 3. Department of Behavioural Science and Health, University College London, London, UK. 4. Center for Bioethics, Harvard Medical School, Boston, USA. 5. North East Thames Regional Genetics Services, Great Ormond Street Hospital, London, UK. 6. Patient Reported Outcomes, Value and Experience (PROVE) Center, Brigham and Women's Hospital, Boston, USA; Department of Surgery, Harvard Medical School, Boston, USA. 7. North East Thames Regional Genetics Services, Great Ormond Street Hospital, London, UK; UCL Great Ormond Street Institute of Child Health, UK.
Abstract
OBJECTIVE: Whole-genome sequencing is being implemented in research and clinical care, yet tools to assess patients' knowledge are lacking. Our aim was to develop a robust measure of whole-genome sequencing knowledge suitable for patients and other stakeholders including research participants, public, students, and healthcare professionals. METHODS: An initial set of 17 items was developed via an iterative process including literature review, expert consultation, focus groups, and cognitive interviews with patients, and then administered to 243 individuals. We used exploratory factor analysis and item-response theory to confirm the psychometric suitability of the candidate items for assessing whole-genome sequencing knowledge. RESULTS: There was a strong main component after removing 5 items with low factor loadings. Item and scale homogeneity was achieved using Mokken scale analysis. Three further items were removed because they were misfits, inverse duplicates or resulted in local dependency. The remaining nine items fitted the two-parameter logistic IRT model which achieved excellent fit to the observed data. Cronbach's alpha was 0.79 indicating acceptable reliability. CONCLUSION: The KOGS, developed using a rigorous psychometric approach, is a brief and reliable tool. PRACTICE IMPLICATIONS: The KOGS may prove useful for researchers and healthcare professionals using whole-genome sequencing with patients and other stakeholders.
OBJECTIVE: Whole-genome sequencing is being implemented in research and clinical care, yet tools to assess patients' knowledge are lacking. Our aim was to develop a robust measure of whole-genome sequencing knowledge suitable for patients and other stakeholders including research participants, public, students, and healthcare professionals. METHODS: An initial set of 17 items was developed via an iterative process including literature review, expert consultation, focus groups, and cognitive interviews with patients, and then administered to 243 individuals. We used exploratory factor analysis and item-response theory to confirm the psychometric suitability of the candidate items for assessing whole-genome sequencing knowledge. RESULTS: There was a strong main component after removing 5 items with low factor loadings. Item and scale homogeneity was achieved using Mokken scale analysis. Three further items were removed because they were misfits, inverse duplicates or resulted in local dependency. The remaining nine items fitted the two-parameter logistic IRT model which achieved excellent fit to the observed data. Cronbach's alpha was 0.79 indicating acceptable reliability. CONCLUSION: The KOGS, developed using a rigorous psychometric approach, is a brief and reliable tool. PRACTICE IMPLICATIONS: The KOGS may prove useful for researchers and healthcare professionals using whole-genome sequencing with patients and other stakeholders.
Authors: Celine Lewis; James Buchannan; Angus Clarke; Emma Clement; Bettina Friedrich; Jillian Hastings-Ward; Melissa Hill; Ruth Horn; Anneke M Lucassen; Chris Patch; Alexandra Pickard; Lauren Roberts; Saskia C Sanderson; Sarah L Lewell; Cecilia Vindrola-Padros; Monica Lakhanpaul Journal: NIHR Open Res Date: 2021-11-22
Authors: Michael D Linderman; Sabrina A Suckiel; Nathan Thompson; David J Weiss; J Scott Roberts; Robert C Green Journal: Public Health Genomics Date: 2021-05-31 Impact factor: 2.132
Authors: Celine Lewis; Bao S Loe; Chris Sidey-Gibbons; Christine Patch; Lyn S Chitty; Saskia C Sanderson Journal: Clin Genet Date: 2019-07-30 Impact factor: 4.438
Authors: Dana Watnick; Jacqueline A Odgis; Sabrina A Suckiel; Katie M Gallagher; Nehama Teitelman; Katherine E Donohue; Bruce D Gelb; Eimear E Kenny; Melissa P Wasserstein; Carol R Horowitz; Siobhan M Dolan; Laurie J Bauman Journal: HGG Adv Date: 2021-02-03