Psychometric evaluation of the Finnish version of the Assessment of Work Performance (AWP-FI).

Background: Assessment of work ability is complex yet crucial in occupational health and vocational rehabilitation. Evaluating psychometric properties is essential to ensure the accuracy of assessment tools in this field. Aim: The aim of this study was to investigate the psychometric properties of t...

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Detalles Bibliográficos
Publicado en:Scandinavian Journal of Occupational Therapy Vol. 32; no. 1; pp. 1 - 9
Autores principales: Nyman, Jennie, Sandqvist, Jan, Pihlava, Jari, Ekbladh, Elin, Yngve, Moa
Formato: research tables/charts Journal Article
Publicado: Springer Nature Jan2025
Acceso en línea:Ver este registro en EBSCOhost
Descripción
Sumario:Background: Assessment of work ability is complex yet crucial in occupational health and vocational rehabilitation. Evaluating psychometric properties is essential to ensure the accuracy of assessment tools in this field. Aim: The aim of this study was to investigate the psychometric properties of the Finnish version of the Assessment of Work Performance (AWP-FI) with a focus on construct validity. Material and Methods: The AWP assesses a client's observable working skills during work performance in three domains: motor skills, process skills, and communication and interaction skills. Ninety-four AWP-FI assessments were performed by 17 occupational therapists in Finland. A Rasch analysis was conducted to evaluate the psychometric properties. Results: The AWP-FI presented an overall fit to the Rasch model with acceptable item-fit statistics and items performing stably between gender, work tasks, and observation types. A suboptimal targeting was evident and issues concerning local dependency among items and indications of multidimensionality were indicated. Conclusion: This study provides an initial validation of the AWP-FI, demonstrating generally acceptable psychometric properties, suggesting that the AWP-FI is valid and reliable for assessing work performance. Further testing is recommended to address the identified issues with local dependence and suboptimal targeting.