Indian women’s settlement experiences and the impact on their health: a narrative study in Brampton, Ontario
Bibliographic record
Abstract
Introduction: Women who have immigrated from India experience health decline during settlement in Canada. However, little is known about how their settlement experiences impact their health. Accordingly, our study examined the impact of Indian women's settlement experiences on their health. Method: Eight Indian women aged 25-45 years were recruited for our study through purposive and snowball sampling. Guided by narrative inquiry, data collection included individual interviews and a demographic survey. Subsequently, data analysis was completed using Clandinin and Connelly's thematic and holistic method. Results: Narratives described three phases of settlement: 'discovering and seeking', 'compromising and surviving' and 'transitioning and accepting'. Narratives of 'discovering and seeking' described the women's process of exploring and learning about their surroundings and their efforts to obtain information and essential resources. Narratives of 'compromising and surviving' described how the women accepted circumstances below their expectations and applied extraordinary efforts to settle. Narratives of 'transitioning and accepting' depicted women becoming familiar, skilled and supported. This process led to them accepting their new lives and developing hope for a better future. Throughout these phases, women faced social determinants of health (SDOH) challenges and a lack of support which contributed to a decline in their health. Conclusion: Challenges faced during settlement negatively impacted health. When SDOH challenges and distress persist, functional impairment, increased healthcare costs, chronic disease and mortality risk are likely. Alternatively, improved navigation support, culturally appropriate healthcare and equitable employment opportunities could promote Indian women's health during settlement.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.024 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".