Mapping the Language of Social Isolation and Loneliness Among Racialized Older Adults in Canada: A Critical Discourse Analysis of Qualitative Studies
Bibliographic record
Abstract
Social isolation and Loneliness (SIL) are devastating experiences in later life. However, the wide disparity in the experiences of SIL among racialized older adults (ROAs) is underreported, with little attention given to the language that ROAs use when narrating their experience of SIL. With its unique focus on the language used within existing qualitative research in describing SIL among ROAs in Canada, this paper aims to shed light on how the discourse informs ideas about ROAs' lives. Using a critical discourse analysis guided by critical race theory, data was generated from 10 purposively selected qualitative articles conducted with ROAs across four provinces in Canada: Ontario, Quebec, Alberta and British Columbia. Participants identified as Chinese, Black Africans and Caribbeans, Koreans, Spanish, Filipinos, former Yugoslavians, Iranians and Indians. Discourses of barriers, loss and vulnerability, struggles and resistance, (dis)connection, and settlement experience were dominant themes. The findings highlight the interrelated and linked aging experiences among diverse ROAs regardless of ethnicity, race, culture, province of residence, and country of birth. Therefore, to mitigate their overall experience of SIL, a conducive and enabling environment encompassing research, policy, and practice that promotes the thriving of ROAs in Canada is warranted.
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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 | 0.022 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.032 | 0.019 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| 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".