EXPLORATION OF RESETTLEMENT EXPERIENCES AND TRANSITIONS TO EMPLOYMENT OF SOUTH ASIAN WOMEN
Bibliographic record
Abstract
Background: Immigrants of South Asian origin are one of the largest groups immigrating to Canada, and females in this group struggle more with resettlement than their male counterparts. There is limited research that understands the nature of occupational transitions in resettlement and the experiences of South Asian women as they transition to employment.\nMethods: A phenomenological qualitative study drawing on critical and occupational theoretical perspectives was used to gain a deeper understanding of experiences of occupational transitions to employment of South Asian women. Narrative interviews with four women were completed and interpretive phenomenology was used to guide the analysis.\nFindings: Three phases of the resettlement process were found: Unsettlement, Rebuilding and Settlement. These phases underscored the importance ofthe relationship between micro and macro levels in the study of occupational transitions. Productive work was found as an important construct that facilitated Settlement through providing structure and emotional stability for daily routines and family life.\nImplications: This study’s findings have implications for the future study of occupational transitions by including a comprehensive focus on the micro and macro issues affecting transitions, barriers and facilitators to success. Findings also have implications for program development in facilitating the occupational transitions of South Asian women.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".