Literature review of the use of Qualitative Behaviour Assessment with a fixed list of terms - Czycholl, Irena; Skovlund, Cecilie Ravn; Forkman, Björn (Frontiers in Veterinary Science, 2026)
Qualitative Behaviour Assessment (QBA) is a method that is used to assess emotional states in animals, either based on a list of pre-established terms (fixed list; FL) or developed through Free-Choice-Profiling. Although FL QBA was originally developed for welfare assessment of farm animals, it is nowadays also used for various species and other sectors. This is, amongst others, because QBA contributes a unique ‘whole-animal’ insight into animal experience that complements other measures and its high feasibility along with a general lack of available indicators of positive emotional state. This has led to a number of different usages and applications of FL QBA of which an overview (e.g., exact methodology used, statistical analysis, purpose and aim) so far does not exist. The aim of this review is to provide an overview of the studies that have applied FL QBA, the species it has been used for and the corresponding lists, what the QBA was used for (purpose), as well as about the various methodological approaches. Web of Science was searched between October 2023 and February 2024 for all peer-reviewed publications on FL QBA. 193 publications met inclusion criteria and were included for final review. The most common aim of use of QBA was on-farm welfare assessment, followed by measuring behavioural/emotional responses to specific events and temperament assessment/behavioural profiling. FL QBA was mainly identified for farmed animals (cattle, pigs, hens, sheep, goats, buffaloes and salmon) but also for working and companion animals (horses and donkeys, dogs and cats) as well as various exotic species in other contexts such as zoological institutions (brown bears, polar bears, elephants, dolphins, gorillas, peccaries and pampas deer). No FL QBA use was identified for laboratory animals. Methodological approaches varied greatly, including differences in term generation, observation methods (e.g., individual-vs. group observation and time spent observing), level of training and inter-observer reliability, and statistical analysis. Moreover, the level of reporting also varied greatly. In sum, this review provides a full overview of the current state of FL QBA including a list of all FL used which is important for the future development, refinement and standardisation of the method.