Internationally educated nurses: profiling workforce diversity
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
AIM: Nurses with diverse educational and cultural backgrounds are likely to adapt differently to new workforces. The aim of this study was to provide a profile of nurses educated in different countries who are employed in a major settlement jurisdiction. BACKGROUND: Despite difficulties in measuring its magnitude, it is evident that nurse migration has increased as a result of globalization. Major destinations for internationally educated nurses (IENs) include the USA, Canada, the UK, Australia and the Gulf States. Chief donor countries include the Philippines, India and other South Asian countries. Half of all IENs registered in Canada work in the province of Ontario. METHODS: Published literature and secondary data were used to profile cohorts of nurses educated in different countries who are employed in the Ontario workforce. FINDINGS: Statistics available on IENs in Ontario reveal a largely urban settlement pattern. There are major differences among IEN cohorts in terms of age, gender, work status, and type and place of employment. DISCUSSION AND CONCLUSIONS: Although IENs resident in Ontario could not be quantified, a relatively detailed description of IENs in the workforce was possible. Comparison of nurse cohorts indicated that generalizations about IENs should be made with caution. Changes in regulatory conditions have a significant effect on IEN employment. Difficulties associated with international educational and regulatory differences illustrate the need to create global nursing standards. Further investigation of differences in workforce profiles should provide insights leading to improved utilization of IENs.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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