Micro-Business and Occupational Stress Process: Occupational Demands, Job Autonomy, and Depression in Korean Immigrant Micro-business Owners and Paid Employees in Toronto, Canada.
Bibliographic record
Abstract
INTRODUCTION: Immigrant status is a pivotal determinant of access to employment and job-related physical and psychological health in North America. Due to labor market challenges, large proportions of Asian immigrants turn to self-employment in low-yielding service sectors. Although occupational stress has long been a central focus of psychological research, few studies investigate how immigrant micro-business owners (MBOs) respond to their unusually demanding occupation, or how their unresolved occupational stress manifests in psychological distress or disorders. Based on a Job Demands-Control (JD-C) model, this study compares MBOs to paid employees on depressive symptoms, occupational demands, and job autonomy. METHODS: Data were derived from a cross-sectional survey of 1,288 Korean immigrant workers (MBOs, professionals, office workers, and manual workers) aged 20 to 64, living in Toronto and surrounding areas. Person to person interviews were conducted between March and November 2013. RESULTS: Among the four occupational groups, MBOs reported the greatest physical, psychological, and emotional demands, as well as job autonomy. MBOs also had a higher level of depression than professional and office workers. While all three types of occupational demands were significantly associated with depressive symptoms, the influence of emotional demand was greater for MBOs than professional employees. An inspection of autonomy-stress interactions suggested autonomy significantly moderated against the impact of emotional demand ( P <.022) and psychological demand ( P <.006) on depressive symptoms. We also found the stress-moderating impact of job autonomy was more clearly highlighted among MBOs than paid employees.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".