New‐immigrant women in urban Canada: insights into occupation and sociocultural context
Bibliographic record
Abstract
Recent statistics have shown that women from South Asia comprise one of the largest sub-groups of immigrants to enter Canada. The majority of this population has settled in the city of Toronto. As immigrants adapt to new physical, social, political, and economic environments in a new country, they are also subject to changes in occupational roles and expectations. Little research has been conducted with new immigrant women from South Asia from an occupational adjustment perspective in Canada. This qualitative study sought to understand the adjustment experiences of immigrant women from South Asia regarding the influence of a Canadian urban environment on their occupations. Twelve recently immigrated women from South Asia to Canada were interviewed about their experiences of living in the city of Toronto with respect to their adjustment to a new environment and engagement in new daily occupations. Using a modified grounded theory approach to analysis, results from the study revealed many challenges these women face and the major factors that aid in the adjustment process. A framework for understanding occupational adjustment in new immigrants is discussed with implications for occupational therapy theory and practice.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.026 | 0.006 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".