Understanding Skilled Immigrants’ Mental Health: The Impacts of Underemployment in Post-Migration Life
Bibliographic record
Abstract
Economic immigration is a powerhouse for Canadian labour force, and foreign trained highly skilled workers are a crucial part of the economy. In 2019, 58% of permanent resident admissions in Canada were economic immigrants; 48% of these economic immigrants were highly skilled immigrants who hoped to utilize their foreign training and work experience to contribute to Canadian market. However, many skilled immigrants encounter barriers when searching for professions consistent with their skill sets, and many end up working in low-skilled sectors of employment where they are overqualified—a situation termed underemployment. This literature review investigates the underemployment experience of skilled immigrants and the impact of underemployment on the mental health of skilled immigrants. A systemic search of the literature was undertaken through PubMed, PsychINFO, Google Scholar, CityU Library, and Elicit. Findings suggest that underemployment is associated with psychological distress such as depressive symptoms, anxiety, and low self-esteem. Multiple themes emerged from the literature: the unique acculturation experience of skilled immigrants, loss and grief caused by underemployment and migration, somatization of mental health issues, and barriers to accessing mental health services. After providing a review of the literature related to these themes, this paper discusses implications for counselling psychology, directions for future research, and recommendations for mental health professionals to effectively support underemployed skilled immigrants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".