New Zealand’s Migrant Asian Nurses: Recent Trends, Future Plans
Bibliographic record
Abstract
Nurses make up the largest component of the health workforce. New Zealand currently has around 47 thousand registered and enrolled nurses, of whom, about a quarter originally trained overseas. For the last six consecutive years, new overseas registrations have approximately equalled or exceeded the number of New Zealand trained new registrations, with 19 per cent of all new registrations in 2013 coming from India, China and South East Asia. The average age of nurses in New Zealand is now 48, and attracting and retaining younger nurses (both New Zealand and overseas educated) will be essential if the predicted increase in demand for nurses due to an ageing population coincides with peak retirement of older nurses in approximately fifteen years. Using multiple data sources, this paper summarises these changes and reports the findings related to career plans reported by Asian respondents from a recent New Zealand Nurses Organisation (NZNO) survey (the New 2 NZNO study) that have potentially serious implications for the sustainability of New Zealand’s nursing workforce. Foremost among these is that modelling assumptions currently proposed to ensure an adequate nursing workforce are likely to severely overestimate the effectiveness of relying on internationally trained nurses to fill a predicted skill shortage long term.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".