The prevalence of over-qualification and its association with health status among occupationally active new immigrants to Canada
Bibliographic record
Abstract
OBJECTIVES: Occupational over-qualification refers to a situation where an individual's occupational status is lower than would be expected by their training, skills, or experience. The objective of this study is to examine the prevalence of three dimensions of over-qualification among a cohort of new immigrants to Canada, and the associations between each dimension of over-qualification with changes in general and mental health status over a four-year period. DESIGN: This study utilized data from the Longitudinal Survey of Immigrants to Canada. For the purpose of this study, we restricted our sample to those employed respondents who worked before coming to Canada, were planning on working after immigration, were in good health at baseline and were interviewed at 4 years post-arrival (N=2685). We defined three measures of over-qualification based on occupational attainment at 4 years relative to: level of education, previous work experience, and occupational expectation upon arrival in Canada. Regression models explored the associations between each dimension of over-qualification and change in self-reported general and mental health adjusting for a variety of immigrants' personal and immigration-related characteristics. RESULTS: Four years after arriving in Canada, 51.6% of immigrants were overqualified for their jobs based on their education levels, with a lesser extent overqualified based on experience (44.4%) or expectations (42.8%). Respondents experiencing any dimension of over-qualification were more likely to report a decline in mental, but not general, health. These relationships were only mildly attenuated after adjustment for other possible confounding variables. Inclusion of job satisfaction and perceptions of employment situation mediated these relationships to a large extent suggesting they are primary pathways through which over-qualification influences mental health. CONCLUSIONS: On average, occupationally active immigrants who were overqualified for their attained occupations in Canada had poorer mental health status than other immigrants 4 years after arrival in Canada. Effective policies and services that support opportunities for immigrants to use their skills appropriately in the Canadian labor market have important labor, social- and health-related consequences.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".