A Narrative Inquiry into the Professional Identity Shifts of Skilled Immigrants
Bibliographic record
Abstract
The challenges and barriers that skilled immigrants face in Canada lead many of them to forego pursuing employment in their fields of expertise. This unexpected disruption in their professional lives has been known to have a profound effect on their sense of self that has been coined a loss of professional identity. This loss has been associated with negative impacts to their well-being, including feelings of frustration, hopelessness, depression, and a strong sense of betrayal by the Canadian government. The bulk of the research on this population has focused on understanding the barriers that thwart their labour market integration. Comparatively, few studies have explored how skilled immigrants negotiate this loss and reconstitute their professional sense of self. Therefore, the objective of this study was to examine the professional identity shifts of skilled immigrants. Using narrative inquiry, I documented the stories of seven skilled immigrants about their job search experiences and their impact on their view of themselves as professionals. In addition, I borrowed elements from critical discourse analysis to explore the role that discourses about skilled immigrants in Canada played in how participants made sense of their job search experiences. Data collection involved a 90-minute semi-structured interview with each participant. The themes that emerged from each interview were organized into a short narrative of each participant’s career experiences pre and post migration. Across-case analysis of these narratives revealed common threads of prosperous careers in their home countries; expectations of professional success in Canada; a rupture in their professional lives; the rekindling of their professional selves; and resisting hopelessness in the face of great difficulties. Reflecting about these common threads led me to conclude that participants’ stories communicated a sense of perseverance and hope, but their accounts were mired with tensions tied to the unhelpful influence of discourses about skilled immigrants on their attempts at professional integration. In an effort to avoid relinquishing their careers, participants resisted being construed as deficient and attempted to negotiate the requirement of Canadian experience. From these broader themes I drew implications for employment counsellors, the immigrant-service sector, and investigators whose research focuses on this population.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.027 | 0.022 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| 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".