Bibliographic record
Abstract
AIM: To analyse critically the waste in human capital of Ontario's internationally educated nurses resulting from unemployment or underemployment. BACKGROUND: Globalization of the nursing workforce is resulting in more and more internationally educated nurses migrating to Canada every year. In Ontario, internationally educated nurses represent 11% of the total nursing workforce but many are unable to become registered in Ontario. According to the College of Nurses of Ontario (CNO), 40% of internationally educated nurse applicants never complete the application process and thus never become Registered Nurses in Ontario. Systemic barriers that prevent registration in Ontario can result from any of the seven requirements for completing the application process. The inability of internationally educated nurses to become registered is significant, considering the national and global nursing shortage. In addition, the inability to become registered results in tremendous waste of human capital, especially in developing countries that have invested financially in educating nurses. Although several programmes have been implemented in Ontario for internationally educated nurses, barriers exist in the design and administration of these programmes, and these are described. DATA SOURCE: An opinion piece of international interest and a human interest piece. CONCLUSION: Internationally educated nurses face significant barriers, which prevent their integration into the Ontario healthcare system. Several policy and management strategies are outlined that could be implemented to ease their integration into the Ontario healthcare system.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".