Migrant Care Labour, Covid-19, and the Long-Term Care Crisis: Achieving Solidarity for Care Providers and Recipients
Bibliographic record
Abstract
Abstract Globally there is a care crisis in terms of the quantity of care needed for an aging population and the quality of both the care provided and work conditions of those providing this care. The COVID-19 pandemic has exposed and heighted this crisis of care. In this chapter we review the issue with a particular focus on long-term care (LTC) facilities and the type and skill mix of labour, including the degree to which immigrant workers are over-represented in this sector. We offer some conceptual reflections on elder care as a matter of social justice and ethics in terms of those needing and providing care. These concerns take on a specific global dimension when we understand the transnationalisation of care, or the care provisioning function of what are termed global care chains. We contextualise how this migrant labour is positioned within this sector through international comparisons of funding models for LTC, which also allows us to understand the structural conditions within which this globally-sourced workforce is positioned. We then highlight two significant contributing factors to the current LTC crisis that were intensified and exposed during the COVID-19 pandemic using Ontario, Canada, as an example: the role of the private sector and the unsustainable extraction of profits from this service, and the gendered and racialised devaluing of migrant labour so essential to the sector.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".