The Experience of Social Exclusion and the Path to Inclusion from the Perspectives of Immigrant and Refugee Women in the Niagara Region
Bibliographic record
Abstract
Social inclusion is a common goal for equitable access to resources for living, is important to health and wellbeing, and is supported by most Western or developed nations. Despite this, immigrant and refugee women continue to be excluded from social, cultural, economic, civic, and political participation during and after settlement. Most research exploring the context of social exclusion has reinforced that some groups experience greater exclusion than others in any given population, for example, immigrant women. The purpose of this study was to gain insights by exploring the experiences of social inclusion and exclusion and recommendations from the perspectives of immigrant and refugee women, as well as community service workers in the Niagara Region, Canada. Utilizing qualitative descriptive inquiry underpinned by intersectionality theory along with thematic analysis, we interviewed 10 immigrant and refugee women and 14 community service workers. Five themes were generated: (1) gendered nature of exclusion; (2) levels of exclusion; (3) paving a path for self; (4) formal inclusionary processes; and (5) informal inclusionary processes. The findings will be used to guide community action and may be transferable to community organizations that serve immigrant and refugee women in similar community contexts, with the aim of enhancing collaborations to advance health equity and inclusion.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.017 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".