Highlights from Help Wanted? Providing and Paying for Long-
Bibliographic record
Abstract
2007. This share is expected to at least double by 2050. � Over the same period, the demand for LTC workers as a share of the working population is set to increase by at least 1.5 times, raising the questions of whether current policies to attract and retain care workers are sufficient to meet future demand. � In some OECD countires, foreign-born care workers form a structural component of the long-term care (LTC) workforce. In the United States, about 25 % of direct care workers are foreign-born (EU-15 average of about 10 % of workers in the health and other community service sector), while about 4 per cent of the registered nurses are foreign-trained (about 6 % among selected OECD countries). Still, options for legal entry of foreign-born are limited. Only a few countries, such as Australia and Canada, have immigration programmes that can apply to long-term care workers. � The United States is one of the very few OECD countries – together with England – where LTC coverage is provided through safety-net programmes and targeted to the poor, as part of Medicaid. With France, the United States has one of the most developed markets of private LTC insurance. However, it remains a niche product, which principally serves the segment of the population with relatively higher
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.130 | 0.028 |
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".