The Globalisation of the Nursing Workforce: Barriers Confronting Overseas-Qualified Nurses in Australia
Bibliographic record
Abstract
Recent decades have coincided with the rapid globalisation of the nursing profession. Within Australia there has been rising dependence on overseas qualified nurses (OQNs) to compensate for chronic nurse shortages related to the continued exodus of Australian nurses overseas and to emerging opportunities in other professions. Between 1983/4 and 1994/5, 30 544 OQNs entered Australia on either a permanent or temporary basis, counter-balancing the departure overseas of 23 613 locally trained and 6519 migrant nurses (producing a net gain of just 412 nurses in all). The period 1995/6–1999/2000 saw an additional 11 757 permanent or long-term OQN arrivals, with nursing currently ranked third target profession in Australia’s skill migration program, in the context of continuing attrition among local nurses. This pattern of reliance on OQNs is a phenomenon simultaneously occurring in the UK, the US, Canada and the Middle East — the globalisation of nursing reflecting not merely Western demand but the growing agency and participation of women in skilled migration, their desire for improved quality of life, enhanced professional opportunity and remuneration, family reunion and adventure.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".