CHARACTERIZING OCCUPATIONAL COMPLEXITY - Insights from the Canadian Longitudinal Study on Aging - Abstract

Sep 29, 2026

Using data from the Canadian Longitudinal Study on Aging (CLSA), the study examines how occupational complexity may relate to cognitive trajectories after retirement. Previous work suggests cognitive heterogeneity, and occupational complexity is linked to cognitive reserve theory, yet its structure within this cohort was unclear. Researchers analyzed 8,243 workers by classifying occupations via the Dictionary of Occupational Titles (DOT) and applying principal component analysis to extract underlying dimensions. Results reveal two dimensions—Coordination and Support, and Autonomous Management—accounting for 82.5% of variance.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages1

Reading Time3 min

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Abstract

Data source and research aim

Methods and occupational complexity scoring

PCA results: two dimensions

Implications for future research

856 Innovation in Aging, 2025, Vol. 9, No. S2 Abstract citation ID: igaf122.2899 CHARACTERIZING OCCUPATIONAL COMPLEXITY: INSIGHTS FROM THE CANADIAN LONGITUDINAL STUDY ON AGING Catherine Gosselin1, Annick Parent-Lamarche2, and Benjamin Boller3, 1. Université du Québec à Trois-Rivières, Repentigny, Quebec, Canada, 2. Université du Québec à Trois-Rivières, Trois-Rivieres, Quebec, Canada, 3. Université du Québec à Trois-Rivières, Trois-Rivières, Quebec, Canada Using data from the Canadian Longitudinal Study on Aging (CLSA), we found that previous research suggests cognitive trajectories following retirement exhibit heterogeneity (Gosselin & Boller, 2022). One potential explanatory factor is the complexity of one’s profession, which has been linked to cognitive reserve theory. However, the specific structure of occupational complexity within this cohort remains to be fully characterized. This study aims to identify and define the key dimensions of occupational complexity among participants from the CLSA using a data-driven approach. The sample consisted of 8,243 workers (M = 54.18, SD = 6.09) . Occupational information was classified using the Dictionary of Occupational Titles (DOT) to derive complexity scores based on levels of interaction with data, people, and materials. A principal component analysis (PCA) was conducted to extract underlying dimensions of occupational complexity. PCA revealed two distinct dimensions of occupational complexity, explaining 82.5% of the variance: the first,“Coordination and Support”(51.4%), encompasses professions characterized by high levels of social interaction, mentoring, and personnel management (e.g., psychologists) . The second,“Autonomous Management”(31.1%), comprises professions requiring elevated autonomy indecision-making and the performance of specialized, often technical, tasks (e.g., engineers). These dimensions highlight distinct patterns of professional task demands within the CLSA cohort. By identifying key occupational complexity profiles, this study enhances our understanding of the professional backgrounds within the CLSA. These findings provide a foundation for future research examining the potential long-term cognitive and health-related implications of occupational characteristics.

FAQs

What research question does the abstract address?

It aims to identify and define the key dimensions of occupational complexity among participants in the CLSA using a data-driven approach.

How were occupational complexity measures derived?

Occupational information was classified using the Dictionary of Occupational Titles (DOT) to derive complexity scores based on interaction levels with data, people, and materials.

What did the principal component analysis find?

PCA revealed two distinct dimensions of occupational complexity, explaining 82.5% of the variance: Coordination and Support and Autonomous Management.

Sep 29, 2026

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