FOREIGN-BORN AND MIGRANT DIRECT CARE WORKERS: A SOLUTION TO THE GLOBAL DEMAND FOR ELDERCARE
Bibliographic record
Abstract
This presentation critically reviews issues related to the increasing reliance on foreign born/migrant workers to address the eldercare workforce crisis in high and middle income countries. It begins with a brief discussion of the factors underlying the growing demand for and lack of direct care workers in most of the developed world, followed by a description of the percent and countries of origin of foreign-born workers employed in Europe, the United States, Canada, Australia and Asia. The benefits of and challenges to this growing reliance on a foreign-born workforce for elderly consumers, their families, workers and the host and origin countries are identified. This session concludes with examples of how organizations such as the WHO, the ILO and the OECD are attempting to address the challenges to this global transfer of human capital through the development of standardized recruitment/retention practices, ethical guidelines, training programs and employer registries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".