Characterizing Occupational Complexity: Insights from the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
Abstract 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 in decision-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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".