Presentation Information
[SY-113-04]Streamlining Cultural Consensus Analysis in Mental Health Research: Potentials and Limitations
*Mariana Borges da Fonseca1,2,4, Andrew Ryder1,2,3,4 (1.Concordia University(Canada), 2.Centre for Clinical Research in Health, Concordia University(Canada), 3.Culture & Mental Health Research Unit, Jewish General Hospital(Canada), 4.Culture, Health and Personality Lab, Concordia University(Canada))
Keywords:
Mental health,Cultural Consensus Analysis,Culture-Bound Syndromes,Idioms of distress
Cultural Consensus Analysis (CCA) is a mixed-method approach increasingly used to investigate psychiatric conditions and idioms of distress across cultural groups. Traditionally composed of three stages—free-listing, pile sorting, and a consensus questionnaire—CCA enables rapid yet in-depth exploration of how groups structure beliefs, symptoms, and coping strategies related to mental health. These insights are key to improving culturally responsive care.
Recent research proposes a shortcut by applying CCA directly to free-list data, bypassing the second and third stages. Although promising, this approach remains underinvestigated in health research, and statistical techniques vary. Three primary methods have been used to analyze free-list data for cultural consensus: (1) factor analysis of inter-informant agreement, (2) Bayesian Cultural Consensus Theory (BCCT), and (3) weighted averages based on individual cultural competence.
We applied and compared the three methods to free-list data collected in Brazil (n = 39) and Canada (n = 35). The data was generated from 14 prompts designed to investigate cultural models of masculinity and femininity. While all three methods yielded convergent results, each provides unique contributions. Factor analysis indicates the extent to which each item mentioned loads onto the first factor, reflecting its relative importance within the shared cultural model. Bayesian analysis estimates the posterior probability that a given category is part of the group's consensus model. Lastly, the weighted average method offers a more intuitive approach by weighting each participant’s response to the prompt by their cultural competence, clarifying how the frequency of category mention and the cultural competence of participants inform the models.
While the CCA shortcut can be valuable for rapid assessments in applied health settings, it cannot replace the interpretative depth of the three-step method. Instead, it should be viewed as a preliminary tool—useful for enhancing the analytic robustness of the free-listing phase while preserving the need for deeper cultural analysis.
Recent research proposes a shortcut by applying CCA directly to free-list data, bypassing the second and third stages. Although promising, this approach remains underinvestigated in health research, and statistical techniques vary. Three primary methods have been used to analyze free-list data for cultural consensus: (1) factor analysis of inter-informant agreement, (2) Bayesian Cultural Consensus Theory (BCCT), and (3) weighted averages based on individual cultural competence.
We applied and compared the three methods to free-list data collected in Brazil (n = 39) and Canada (n = 35). The data was generated from 14 prompts designed to investigate cultural models of masculinity and femininity. While all three methods yielded convergent results, each provides unique contributions. Factor analysis indicates the extent to which each item mentioned loads onto the first factor, reflecting its relative importance within the shared cultural model. Bayesian analysis estimates the posterior probability that a given category is part of the group's consensus model. Lastly, the weighted average method offers a more intuitive approach by weighting each participant’s response to the prompt by their cultural competence, clarifying how the frequency of category mention and the cultural competence of participants inform the models.
While the CCA shortcut can be valuable for rapid assessments in applied health settings, it cannot replace the interpretative depth of the three-step method. Instead, it should be viewed as a preliminary tool—useful for enhancing the analytic robustness of the free-listing phase while preserving the need for deeper cultural analysis.