Aging in a Foreign Country: Voices of Iranian Women Aging in Canada
Bibliographic record
Abstract
Older Iranian women, who immigrated to Canada in later adulthood, experience unique issues as they age. In order to better understand this experience, in-depth, personal and semi-structured interviews were conducted with five immigrant/refugee Iranian women who immigrated to Canada in their later life. Analysis revealed that although each woman's story conveyed individual differences and idiosyncrasies, all the stories highlighted the critical interweaving of the aging experience and the immigration experience: neither experience could be understood in isolation of the other; each aspect gave meaning to the other experience. Two interrelated messages dominated the women's stories: first was the importance of each woman's immigration story for grounding her experience of the aging process in Canada. Second, each woman's personal story suggested that the immigration experiences were accorded priority for accounting for her experiences in Canada. Specifically, cultural identity (i.e., social class, education, religious affiliation and immigration status) offered a valuable cloak for overshadowing the force of the aging process and the aging process emerged as an elusive force that lurked in the background without ever being fully acknowledged or given power in their lives. The implications of these findings in relation to theory development on intersectionality and professional practice are discussed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.007 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".