Doing well in Canada : a critical incident study of immigrants' experiences
Bibliographic record
Abstract
In response to Canada’s shrinking labour force, experts are calling for a more diverse, flexible, adaptable workforce. Many recommend that employers hire immigrants. However, employers may not recognize immigrants’ qualifications or experience. Well educated, experienced immigrants often resort to low paying, unskilled jobs. Guided by a positive psychology perspective, the main focus of this qualitative study was to 1) explore the changes inherent in immigration and challenges to employment that immigrants may face within the first year of their arrival, 2) investigate the strategies, incidents and factors that helped and hindered immigrants who self identified as doing well with these changes and challenges and 3) determine whether participants have always handled change well. Open-ended interviews were conducted with 17 new immigrants to Canada. Conventional content analysis was employed to develop themes from the context data collected. The Enhanced Critical Incident Technique was used to collect, identify, extract and analyze helping and hindering incidents, as well as a list of what participants wished they had or could have in the future in order to do well. A total of 273 helping incidents, 54 hindering incidents and 59 wish list items were extracted. Ten helping, hindering and wish list categories emerged: 1) Taking Action, 2) Personal Qualities, 3) Life in Canada, 4) Work and Life Experience, Skills and Knowledge, 5) Social Support, 6) Beliefs and Perspectives, 7) Preparation and Research, 8) Government/Agency Support, 9) Plans and Goals, and 10) Financial Support. These results suggest it is possible for new immigrants to do well with the significant life and work related changes and challenges that are embedded in the Canadian immigration experience. Discussion includes participants’ stories as living exemplars of a positive, proactive approach to acculturation, the roles that values and Canadian society played in participants’ doing well, and what coping and doing well may mean to newcomers. Study limitations are discussed along with implications for future research. Implications and recommendations for counselling practice are presented. Finally, policy recommendations are made concerning new immigrants’ employment situation.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.046 | 0.013 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| 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".