Caught between a rock and a hard place: mental health of migrant live-in caregivers in Canada
Bibliographic record
Abstract
BACKGROUND: Canada depends on Temporary Foreign Workers (TFWs), also known as migrant workers, to fill labour shortage in agriculture, hospitality, construction, child/senior care, and other low-skilled occupations. Evidence shows that TFWs, especially women live-in caregivers (LC), constitute a vulnerable population. Their health is compromised by the precarious and harsh working and living conditions they encounter. There is a paucity of research on the mental health of LCs, their support systems and access to mental health services. METHOD: In this community-based exploratory study, we used mixed methods of survey and focus groups to explore the work related experiences and mental health of migrant live-in caregivers in the Greater Toronto Area in Ontario, Canada. Convenience and snowball sampling were used to recruit participants. The inclusion criteria were: being 18 years or older, initially migrated to Canada as TFWs under LC program, resided in the Greater Toronto Area, and able to understand and converse in English based on self-report. This paper reports on the focus group results derived from inductive thematic analysis. RESULTS: A total of 30 women LCs participated in the study. Most of them were from the Philippines. A number of key themes emerged from the participants' narratives: (1) precarious migration-employment status (re)produces exploitation; (2) deskilling and downward social mobility reinforce alienation; (3) endurance of hardship for family back home; (4) double lives of public cheerfulness and private anguish; and (4) unrecognized mental health needs. The study results reflected gross injustices experienced by these women. CONCLUSION: A multi-faceted approach is required to improve the working and living conditions of this vulnerable group and ultimately their health outcomes. We recommend the following: government inspection to ensure employer compliance with the labour standards and provision of safe working and living conditions; change immigration policy to allow migrant caregivers to apply for permanent residence upon arrival; the TFWs Program to establish fair wages and subsidized housing so that caregivers can truly access the live-out option; and local ethno-specific, settlement and faith organizations be leveraged to provide TFWs with social support as well as information about their rights and how to access health and social care.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".