Commonalities and Differences in the Experiences of Visible Minority Transnational Carer–Employees: A Qualitative Study
Bibliographic record
Abstract
This qualitative study explored the commonalities and differences among the experiences of visible minority Transnational Carer-Employees (TCEs) before and after COVID-19. TCEs are immigrants who live and work in the country of settlement while providing caregiving across international borders. Purposive and snowball sampling resulted in the participation of 29 TCEs of Pakistani, Syrian, African, and South American origin living in London, Ontario. Thematic analysis of the dataset using the ATLAS.ti software, Version 23.2.1., generated three themes: (1) feelings associated with transnational care; (2) employment experiences of TCEs; and (3) coping strategies for well-being. The results of the secondary analysis conducted herein suggested that there are more similarities than differences across the four cohorts. Many participants felt a sense of satisfaction at being able to fulfill their care obligations; however, a different outlook was observed among some Syrian and African origin respondents, who disclosed that managing care and work is overwhelming. Most TCEs also reported facing limited job options because of language barriers. While various interviewees experienced a lack of paid work and reduced income after COVID-19, a distinct perspective was noted from African descent TCEs as they expressed facing increased work demands after the pandemic. Participants additionally revealed four common coping strategies such as keeping busy, praying, family support, and staying active. Study implications include the promotion of Carer-Friendly Workplace Policies (CFWPs) that can facilitate the welfare of unpaid caregivers. This research is important as it may inform policymakers to create opportunities that may not only foster economic stability of TCEs and the Canadian economy, but also contribute towards a more equitable society.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".