“I am from nowhere”: identity and self-perceived health status of skilled immigrants employed in low-skilled service sector jobs
Bibliographic record
Abstract
Purpose The foreign-born skilled immigrant population is growing rapidly in Canada but finding a job that utilizes immigrants’ skills, knowledge and experience is challenging for them. The purpose of this paper is to understand the self-perceived health and social status of skilled immigrants who were working in low-skilled jobs in the service sector in Ottawa, Canada. Design/methodology/approach In this qualitative study, semi-structured interviews with 19 high-skilled immigrants working as taxi drivers and convenience store workers in the city of Ottawa, Canada were analysed using a grounded theory approach. Findings Five major themes emerged from the data: high expectations but low achievements; credential devaluation, deskilling and wasted skills; discrimination and loss of identity; lifestyle change and poor health behaviour; and poor mental and physical health status. Social implications The study demonstrates the knowledge between what skilled immigrants expect when they arrive in Canada and the reality of finding meaningful employment in a country where international credentials are less likely to be recognized. The study therefore contributes to immigration policy reform which would reduce barriers to meaningful employment among immigrants reducing the impacts on health resulting from employment in low-skilled jobs. Originality/value This study provides unique insights into the experience and perceptions of skilled immigrants working in low-skilled jobs. It also sheds light on the “healthy worker effect” hypothesis which is a highly discussed and debated issue in the occupational health literature.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".