Acculturation and socialization: voices of internationally educated nurses in Ontario
Bibliographic record
Abstract
BACKGROUND: This paper describes a study that explores the experiences of internationally educated nurses (IENs) in their efforts to gain entry to practice as Registered Nurses (RNs) in the province of Ontario, Canada. AIM: The aim was to uncover, in part, the issues related to professional nursing credentialling. METHODS: This study was guided by a biographical narrative (qualitative) research methodology. A convenience sample of 12 IEN students volunteered for this study representing the Philippines, Mainland China, Korea, Ukraine and India. FINDINGS: The findings were that the IENs progress through a three-phase journey in their quest for licensure in Ontario. These phases include: (1) hope - wanting the Canadian dream of becoming an RN in Ontario; (2) disillusionment - discovering that their home-country nursing qualifications do not meet Ontario RN entry to practice; and (3) navigating disillusionment - living the redefined Canadian dream by returning to nursing school to upgrade their nursing qualifications. CONCLUSIONS: Professional regulatory nursing bodies and nursing educators, as well as practising nurses, must be aware of the potentially confusing and unpleasant processes IENs go through as they qualify for the privilege of practising nursing in Ontario.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.010 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".