The Case for Assessing IEN Preparation
Bibliographic record
Abstract
Recent studies warn that Canada will face a critical shortage of as many as 113,000 registered nurses by 2016, in part due to retirement projections in the profession and the downsizing of educational programs and cutbacks in employment opportunities for registered nurses over the past decade. At the same time, health care workers with skills and experience such as nurses with international credentials are seeking pathways that would help them to prepare for registered nursing practice in Canada and Alberta. Addressing the issue of nursing shortages and easing the pathway for individuals to achieve the requirements for Registered Nurse practice in Canada is an important goal for both the Canadian government and the professional nursing community. The Government of Canada has identified the need to develop and employ people more fully in Canada as a means to build a strong skilled workforce. Concurrently, internationally educated nurses (IENs) who have made the move to Canada are seeking programs that would prepare them for registered nursing practice in Canada but currently receive little or no recognition for prior education and experience. Prior Learning Assessment and Recognition (PLAR) has been suggested as a means to reduce the learning recognition gap that exists in Canada and meet the shortage of skilled workers.
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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 | 0.049 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".