Bibliographic record
Abstract
According to the statistical data, immigrant women are less integrated into the Canadian labour market than immigrant men or Canadian-born women, even when accounted for years of education and work experience. Studies of immigrant women’s cross-cultural adjustment and labour market integration have been largely underestimated in the history of research on immigrants’ acculturation. This study explores the factors that contribute to a successful career adjustment of immigrant women in Canada. A comprehensive model of psychological, situational, and social factors designed by Rasouli et al. (2008) and revised by the author was tested on a sample of 101 immigrant women across Canada. Career adjustment was measured positively as job satisfaction and negatively as occupational stress. Career management self-efficacy, problem-focused coping, emotion-focused coping, language self-efficacy, social support, personal income, work experience from Canada, and work experience from the country of origin predicted job satisfaction while emotion-focused coping, career management-self-efficacy, education attained in Canada, and parenthood predicted occupational stress. Career management self-efficacy and education obtained in Canada were in particularly strong predictors of those variables. Implications of the findings and future research directions are discussed.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".