Recommended best practices for construct-centered adaptation of the Harmonized Cognitive Assessment Protocol.

The Harmonized Cognitive Assessment Protocol (HCAP) neuropsychological assessment approach aims to support the collection of harmonizable data on cognitive function for cross-national cognitive aging and dementia research. As the measurement of cognition is sensitive to differences in contextual, cultural, educational, linguistic, social, and other factors that may influence cognitive test performance, HCAP requires adaptation to be appropriate for the contexts in which it is administered. We aim to provide methodological recommendations for the adaptation and implementation of the HCAP and other cognitive data collection tools in novel settings.

We drew from existing guidelines on cross-cultural psychological test adaptation (e.g., the International Test Commission Guidelines for Translating and Adapting Tests); the Cross-Cultural Survey Guidelines; and our shared experiences in adapting, administering, and harmonizing the HCAP in older populations across 10 countries.

Recommendations for HCAP adaptation include phases of preparation (assembling local expert teams; resource planning), implementation (construct-centered adaptation guided by local experts), pre-testing (iterative, mixed-methods approach), and field preparation (creating documentation; interviewer training; planning for ongoing quality assurance).

The HCAP neuropsychological assessment approach has unique considerations for adaptation that balance the needs of local validity with cross-national harmonization. Shared best practices for HCAP adaptation will improve the quality of cross-national HCAP data collection, data harmonization, analyses, and inferences. The HCAP is a useful model for cross-nationally harmonized data collection and research to improve our understanding of cognitive aging and dementia globally.
Mental Health
Care/Management

Authors

Briceño Briceño, Bassil Bassil, Khobragade Khobragade, Ngugi Ngugi, El Bejjani El Bejjani, Ochieng Ochieng, Mejia-Arango Mejia-Arango, Douhou Douhou, Kulisewa Kulisewa, Arce Rentería Arce Rentería, Gross Gross, Jones Jones, Lee Lee, Langa Langa, Kobayashi Kobayashi
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