title: Can we identify the poorest quality of life? Assessing the importance of quality of life using the WHOQOL-100.
abstract: In this study, WHOQOL survey data obtained from 4802 sick and well participants in 15 countries were used to investigate the relationship between judgements about different dimensions of quality of life (QOL) (core scores) and the importance attributed to them. As a theoretical framework, we applied the WHOQOL Group's (1995) definition of QOL which indicates that those who report the very poorest QOL will be least likely to have met their own '...goals, expectations, standards and concerns'. Those with the poorest QOL would therefore be expected to show the biggest difference between core and importance scores, and therefore be distinguishable from respondents whose QOL was poor, better or best. The main effects from overall analyses confirmed that those reporting the largest negative differences tended to report the poorest QOL and also attached a high degree of importance to these dimensions. Evidence for a decreasing differential across the four groups (poorest to best) was confirmed for the majority (18) of facets. However facet level analyses comparing groups with different levels of QOL showed that only five facets distinguished those with the poorest QOL from those whose QOL was poor, so the theory is not well supported. Furthermore the contribution of core-importance facet differences reduced the overall prediction of QOL, when compared with a regression of core scores alone. Importance information about specific facets may have limited potential to be used alongside the main instrument to identify areas of the poorest QOL for clinical or social action.
authors.affiliation: WHO Centre for the Study of Quality of Life, Department of Psychology, University of Bath, Bath BA2 7AY, UK. s.m.skevington@bath.ac.uk
authors.forename: Suzanne M
authors.initials: SM
authors.lastname: Skevington
authors.forename: Kathryn A
authors.initials: KA
authors.lastname: O'Connell
mesh_terms: D000328:Adult; D000704:Analysis of Variance; D001294:Attitude to Health; D003430:Cross-Sectional Studies; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D011594:Psychometrics / Q000379:methods*; D011788:Quality of Life; D012044:Regression Analysis; D012647:Self-Assessment; D018434:Sickness Impact Profile; D014944:World Health Organization