Transnational caring in times of COVID-19: The experiences of visible minority immigrant carer-employees
Bibliographic record
Abstract
Globalization and immigration policies between Canada and immigrant-sending nations have heightened transnational caregiving. The research objective is to explore the experiences of visible minority immigrant transnational carer-employees (VMI TCEs) before and during the pandemic. In this study, participants reside in the mid-sized city of London, Ontario and engage in paid employment or volunteering while providing unpaid care to family members and/or friends abroad. Interviews and arts-based methodology were used to collect data from 29 VMI TCEs from 10 countries. Intersectionality theory informed thematic analysis and three themes emerged: (1) The nuances of providing transnational care, (2) The impact of geographic dislocation on care and wellbeing, and (3) Caregiving during COVID-19. Findings highlight the fluidity of transnational caregiving, in that participants both shape and are impacted by time-space dimensions. Study results may be used to inform culturally sensitive adaptions to the existing standard for organizations to be more inclusive of and accommodating to carer-employees. Findings can also inform the implementation or improvement of programs and services offered by the government, immigration resettlement agencies, employers and other stakeholders working with people who may share similar experiences to VMI TCEs. The creation of accessible and appropriate resources for this group of people will better support them in resettling outside of major urban cities in Ontario and other provinces across Canada.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.011 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 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".