Global migration of internationally educated nurses: Experiences of employment discrimination
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With over 57 countries reporting a critical shortage of healthcare workers worldwide, increasing reliance of developed countries on registered nurses from less developed countries of Africa and Asia has generated a significant policy debate about public health, ethical and policy concerns related to international migration of nurses. Discrimination and unequal treatment faced by migrant nurses is one of the most important issues related to international migration of nurses. This article present a discussion of the broad topics surrounding nurse migration followed by a synthesis of 15 published qualitative and quantitative research articles related to specifically to the subject of employment discrimination experiences of internationally educated nurses in Canada, United Kingdom and the United States. Evidence shows that international nurses often encounter covert and overt discrimination in the workplace. It is important for nurses to be aware of the extent and nature of employment discrimination encountered by migrant nurses. Nursing leaders and policy makers need to ensure that all nurses are treated equally in the workplace.
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.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it