Immigrant and refugee social networks: determinants and consequences of social support among women newcomers to Canada.
Bibliographic record
Abstract
Recent immigrants and refugees (newcomers) vary on many dimensions but do share similar challenges. Newcomers must rebuild social networks to obtain needed social support but often face social exclusion because of their race, language, religion, or immigrant status. In addition, most have limited access to personal, social, and community resources. Effects of situational and personal variables on the benefits and limitations associated with the social networks of female newcomers were explored through interviews and focus groups with 87 women from 7 communities. Using thematic analysis, the authors identify 5 sources of informal support across all 7 communities, which were almost exclusively limited to co-ethnic relationships, and the types of support, limitations, and reciprocity within each. Perceived support was strongest from family and close friends and, when support from close relationships was unavailable, from primary care providers. The results suggest that co-ethnic peer support networks may be overwhelmed in newcomer communities because of their limited size and resources.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".