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BACKGROUND: Incorporating the feedback of expert stakeholders in ontology development is important to ensure content is appropriate, comprehensive, meets community needs and is interoperable with other ontologies and classification systems. However, domain experts are often not formally engaged in ontology development, and there is little available guidance on how this involvement should best be conducted and managed. Social and behavioural science studies often involve expert feedback in the development of tools and classification systems but have had little engagement with ontology development. This paper aims to (i) demonstrate how expert feedback can enhance ontology development, and (ii) provide practical recommendations on how to conduct expert feedback in ontology development using methodologies from the social and behavioural sciences. MAIN BODY: Considerations for selecting methods for engaging stakeholders are presented. Mailing lists and issue trackers as existing methods used frequently in ontology development are discussed. Advisory boards and working groups, feedback tasks, consensus exercises, discussions and workshops are presented as potential methods from social and behavioural sciences to incorporate in ontology development. CONCLUSIONS: A variety of methods from the social and behavioural sciences exist to enable feedback from expert stakeholders in ontology development. Engaging domain experts in ontology development enables depth and clarity in ontology development, whilst also establishing advocates for an ontology upon its completion.
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Ciências do ComportamentoRESUMO
Background: To efficiently search, compare, test and integrate behaviour change theories, they need to be specified in a way that is clear, consistent and computable. An ontology-based modelling system (OBMS) has previously been shown to be able to represent five commonly used theories in this way. We aimed to assess whether the OBMS could be applied more widely and to create a database of behaviour change theories, their constructs and propositions. Methods: We labelled the constructs within 71 theories and used the OBMS to represent the relationships between the constructs. Diagrams of each theory were sent to authors or experts for feedback and amendment. The 71 finalised diagrams plus the five previously generated diagrams were used to create a searchable database of 76 theories in the form of construct-relationship-construct triples. We conducted a set of illustrative analyses to characterise theories in the database. Results: All 71 theories could be satisfactorily represented using this system. In total, 35 (49%) were finalised with no or very minor amendment. The remaining 36 (51%) were finalised after changes to the constructs (seven theories), relationships between constructs (15 theories) or both (14 theories) following author/expert feedback. The mean number of constructs per theory was 20 (min. = 6, max. = 72), with the mean number of triples per theory 31 (min. = 7, max. = 89). Fourteen distinct relationship types were used, of which the most commonly used was 'influences', followed by 'part of'. Conclusions: The OBMS can represent a wide array of behavioural theories in a precise, computable format. This system should provide a basis for better integration and synthesis of theories than has hitherto been possible.
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Perceptual control theory (PCT) approaches the behavior of living systems as though it were a phenomenon of control and systematically assesses the variables that the individual controls using the test for the controlled variable (TCV). PCT may be supported by the minority because the majority of behavior scientists, like most people, can miss the phenomenon of control as it is occurring. An earlier paper reported three studies of a behavior that was known to be a process of control because it had been explicitly instructed. In each case, most observers did not detect the control. Our novel extension of this study used live observation of "actors" and "observers." We tested in pairs 164 participants randomly allocated to each role. The actors completed a two-dimensional compensatory tracking task. To keep a dot at the center of a circle, the movements of a computer mouse needed to vary as the inverse of a disturbance pattern that was an inverted form of the word "hello" in script. The trace of their mouse movements was displayed on the screen-writing the word "hello". As predicted, most observers missed the phenomenon of control; they inferred that the actor's instruction had been to write "hello", rather than to control the dot. In contrast, the actors reported that they had been keeping the dot in the circle and were unaware of having written the word. The TCV analyzes behavior by consistently identifying the controlled variable without relying on heuristic methods used by researchers that can be inaccurate. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
