Do You Prefer Safety to Social Participation? Finnish Population-Based Preference Weights for the Adult Social Care Outcomes Toolkit (ASCOT) for Service Users

• Main Authors: Lien Nguyen, Hanna Jokimäki, Ismo Linnosmaa, Eirini-Christina Saloniki, Laurie Batchelder, Juliette Malley, Hui Lu, Peter Burge, Birgit Trukeschitz, Julien Forder
• Format: Article
• Language: English
• Published: SAGE Publishing 2021-07-01
• Series: MDM Policy & Practice
• Online Access: https://doi.org/10.1177/23814683211027902

Introduction. The Adult Social Care Outcomes Toolkit (ASCOT) was developed in England to measure people’s social care–related quality of life (SCRQoL). Objectives. The aim of this article is to estimate preference weights for the Finnish ASCOT for service users (ASCOT). In addition, we tested for learning and fatigue effects in the choice experiment used to elicit the preference weights. Methods. The analysis data ( n = 1000 individuals) were obtained from an online survey sample of the Finnish adult general population using gender, age, and region as quotas. The questionnaire included a best-worst scaling (BWS) experiment using ASCOT. Each respondent sequentially selected four alternatives (best, worst; second-best, second-worst) for eight BWS tasks ( n = 32,000 choice observations). A scale multinomial logit model was used to estimate the preference parameters and to test for fatigue and learning. Results. The most and least preferred attribute-levels were “I have as much control over my daily life as I want” and “I have no control over my daily life.” The preference weights were not on a cardinal scale. The ordering effect was related to the second-best choices. Learning effect was in the last four tasks. Conclusions. This study has developed a set of preference weights for the ASCOT instrument in Finland, which can be used for investigating outcomes of social care interventions on adult populations. The learning effect calls for the development of study designs that reduce possible bias relating to preference uncertainty at the beginning of sequential BWS tasks. It also supports the adaptation of a modelling strategy in which the sequence of tasks is explicitly modelled as a scale factor.