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Title: Using judgement analysis to identify dietitians’ referral prioritisation for assessment in acute adult services
Authors: Hickson, M
Davies, M
Gokalp, H
Harries, PA
Issue Date: 23-Dec-2016
Citation: Hickson, M., Davies, M., Gokalp, H. et al. Using judgement analysis to identify dietitians’ referral prioritisation for assessment in adult acute services. Eur J Clin Nutr 71, 1291–1296 (2017).
Abstract: Background & Objective Dietitians need to prioritise referrals in order to manage their work load. Novice dietitians may not receive training on prioritisation and could be helped with an evidence-based, effective decision training tool. To develop such a tool, it is necessary to understand how experts make prioritisation decisions. This study aimed to model expert decision making policy for prioritising dietetic referrals in adult acute care services. Methods & subjects Social judgement theory was used to model expert decision making policy. Informational cues and cue levels were identified. A set of case scenarios that replicated dietetic referrals in adult acute services were developed using fractional factorial design approach. Experienced dietitians were asked to make prioritisation decisions on case scenarios. A model was derived using multiple regression analysis to elicit the weighting given to cues and cue levels by the experts when making prioritisation decisions. Results Six cues and 21 cue levels were identified and 60 unique case scenarios were created. Fifty experienced dietitians made decisions on these case scenarios. The “reason for referral” and “biochemistry picture” were the two most influential cues, and “weight history” was the least significant. “Nutritional status”, “presenting complaint” and “previous food intake” had similar weightings. 95.7% of the variability in the experts’ average judgement (adjusted R2=0.93) was predicted by the six cues. Conclusions A model for referral prioritisation in acute adult services described experienced dietitians’ decision making policy. This can be used to develop training materials that may increase the effectiveness and quality of prioritisation judgements.
ISSN: 0954-3007
Appears in Collections:Dept of Computer Science Research Papers

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