Voices of internationally educated nurses: policy recommendations for credentialing
Bibliographic record
Abstract
BACKGROUND: The authors advance general policy recommendations for credentialing Internationally Educated Nurses (IENs) who migrate to practice nursing in developed, high-income countries. While examples are drawn primarily from a qualitative study exploring IEN experiences in Canada, the suggestions presented have broader application to any nursing, or midwifery, internationally educated professionals wanting, or needing, to practice outside their home country of education. Examples of credential processing are drawn from Australia, the European Union, New Zealand, the UK and the USA. METHODS: This study was guided by a biographical narrative (qualitative) research methodology. A convenience sample of 12 IENs volunteered to participate. RESULTS: The IENs offered recommendations based on their personal experiences, all of which have policy implications to make transparent, standardize and harmonize the credentialing processes both prior to, and upon arrival in their destination country. Suggestions are offered to make relevant the content of IEN integration programmes. CONCLUSIONS: The authors also suggested that national immigration agencies and nursing regulatory bodies could better coordinate their activities when processing potential IEN migrant applications.
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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 | 0.082 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.020 | 0.026 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.036 | 0.021 |
| Insufficient payload (model declined to judge) | 0.020 | 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 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".