Immigrant Employment Integration in Canada: A Narrative Review
Bibliographic record
Abstract
International migration has increased globally over the past two decades with migrants currently representing 3.5% of the world's population. As a top destination country, Canada's immigration policy selects some of the most highly skilled and educated migrants. Yet, upon arrival, many face challenges finding employment that matches their skills and qualifications. Extensive research has emerged that seeks to identify barriers to immigrant employment and the determinants of their success in the labour market. This article presents the results of a narrative review of literature on the employment outcomes of recent immigrants to Canada. A total of 33 articles published between 2010 and 2020 were reviewed. Results indicate both individual and contextual causes for a lag in commensurate employment. Characteristics such as immigration class, education and experience, place of settlement, gender, and visible minority status were identified as significant variables. Changing local labour markets, underemployment and underutilization of immigrant skills and the impact of social networks on attaining employment also emerged as important factors. Canada's immigration policy is widely perceived as an exemplar model for attracting skilled migrants. However, when compared to countries like Australia and the United States, newcomers to Canada face challenges integrating into employment efficiently and effectively. Findings from this review offer insight for policymakers to improve employment outcomes through innovations adaptable to local labour markets that promote immigrants' rapid entry into commensurate employment.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.018 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".