Sociodemographic Characteristics of Internationally Educated Nurses Associated With Successful Outcomes in Canada: Quantitative Analysis
Bibliographic record
Abstract
AIMS: This article describes the sociodemographic characteristics of internationally educated nurses since the change in the registration examination in 2015. It aims to investigate the association between internationally educated nurses' sociodemographic characteristics and their successful integration into the nursing workforce in Canada. DESIGN: Cross-sectional and secondary data survey questions. METHODS: This study adopts a cross-sectional and secondary data analysis, utilising data from IENs who engaged with internationally educated nurse initiatives such as the Creating Access to Regulated Employment Centre for Internationally Educated Nurses (CARE) or initiated the registration process with the College of Nurses of Ontario (CNO) in 2015 and after. RESULTS: There were 259 participants, with 155 participants from primary data collection and 104 participants from secondary data sources. Quantitative analysis reveals that most participants are females, under 40 years old, educated in English and hold at least a bachelor's degree in nursing, with 47.3% of internationally educated nurses migrated from India and the Philippines. Significant associations were identified between internationally educated nurses having CARE membership and the currency of nursing practice and their successful outcomes. CONCLUSION: Recognising and addressing the unique needs of IENs is essential for their successful integration into the Canadian healthcare workforce, thereby ensuring resilience and cultural competence in nursing for the future. IMPLICATIONS FOR THE PROFESSION: This analysis highlights the impact of sociodemographic characteristics of internationally educated nurses on their successful outcomes and underscores the diversity and richness they bring to the healthcare landscape. Since internationally educated nurses continue to experience challenges while integrating into the Canadian nursing workforce, these findings have substantial implications for nursing policy, practice, professional development and research.
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.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".