Settlement and Integration of Skilled Immigrants: Implications for Social Work Education and Field Training
Bibliographic record
Abstract
Background: To assist immigrants and to advocate for them, it is very important for social workers to be aware of the challenges faced by various classes of immigrants. Skilled immigrants are economically motivated, professionally trained and vocationally oriented; however, many face significant challenges in their social and economic integration in Canada. Methods: This is a mixed methods study that examines the settlement and integration needs of skilled immigrants. Qualitative data provides an in-depth exploration of the settlement and integration needs of skilled immigrants as understood by immigrant serving agencies, and the quantitative data focuses on gaining an understanding about the areas of unmet settlement and integration needs as experienced by skilled immigrants. Analysis focuses on understanding the settlement and integration needs of skilled immigrants and identifying the gaps in services offered by the major immigrant serving agencies in Calgary. Results: Findings enhance our understanding of challenges faced by skilled immigrants and highlight the need of raising awareness of the current issues and systemic barriers faced by skilled immigrants resulting in underemployment, eventually leading to the brain-waste of highly educated and professionally well-experienced immigrants. Conclusion: Implications of findings for social work education and training, including field education will be discussed and recommendations will be made for social work programs and field training. The paper argues that social work must re-examine the curricula and emphasizes the need to develop creative field training opportunities to prepare future social workers for supporting skilled immigrants as they seek to settle and integrate in Canada.
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 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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".