Settlement Workers Supporting Older Immigrant Women in a Smaller Urban Setting
Bibliographic record
Abstract
Canada relies on immigration to support economic, population and cultural growth. Immigrants face unique challenges related to settlement and integration into Canadian society. Settlement services can offer opportunities to mitigate challenges related to immigration for older immigrant women. However, there is a scarcity of literature exploring the experiences of settlement workers and the needs of older immigrant women in a small urban area. This study addressed gaps in the literature by answering the following question: How do settlement workers support older immigrant women in a smaller urban region setting? A constructivist lens coupled with a qualitative description approach was used. Six semi-structured interviews were conducted with settlement workers. Participants were asked about resources available to them, gaps in the services they provide, utilization of services, barriers to access, and needs of older immigrant women. Data was thematically analyzed. Four major themes emerged from the data: older immigrant women described from the perspective of settlement workers, potential barriers older immigrant women face in accessing services, the know-how of being a settlement worker, and the art of being a settlement worker. In the experiences of settlement workers, older immigrant women have more needs than other immigrant groups, such as younger and male immigrants; they also believe older immigrant women feel comfortable in seeking support from them. In the smaller urban setting, this support becomes crucial as there is usually less informal support available to them. The results of this study improved the understanding of the challenges encountered by settlement workers while working with older adult immigrant women in small urban region areas. Settlement workers identified the need for additional funding to support older immigrant 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 | 0.012 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".