Bibliographic record
Abstract
Previous research indicates that Canadian healthcare workers, particularly long-term care (LTC) workers, are frequently composed of immigrant and racialized/visible minorities (VM) who are often precariously employed, underpaid, and face significant work-related stress, violence, injuries, illness, and health inequities. Few studies, however, have analyzed the contributions and impact of their labor in international contexts and on global communities. For instance, it is estimated that over CAD 5 billion-worth of remittances originate from Canada, yet no studies to date have examined the contributions of these remittances from Canadian workers, especially from urbanized regions consisting of VM and immigrants who live and/or work in diverse and multicultural places like Toronto. The present study is the first to investigate health and LTC workers’ roles and behaviors as related to remittances. The rationale for this study is to fill important knowledge gaps. Accordingly, this study asked: Do health/LTC workers in the site of study send remittances? If so, which workers send remittances, and who are the recipients of these remittances? What is the range of monetary value of annual remittances that each worker is able to send? What is the purpose of these remittances? What motivates the decision to send remittances? This mixed-methods study used a single-case design and relied on interviews and a survey. The results indicate that many LTC workers provided significant financial support to transnational families, up to CAD 15,000 annually, for a variety of reasons, including support for education and healthcare costs, or as gifts during cultural festivals. However, the inability to send remittances was also a source of distress for those who wanted to assist their families but were unable to do so. These findings raise important questions that could be directed for future research. For example, are there circumstances under which financial remittances are funded through loans or debt? What are the implications for the sustainability and impact of remittances, given the current COVID-19 pandemic and its economic effect of dampening incomes and wages, worsening migrants’ health, wellbeing, and quality of life, as well as adversely affecting recipient economies and the quality of life of global communities?
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".