The development of an online instrument for prior learning assessment and recognition of internationally educated nurses: A pilot study
Bibliographic record
Abstract
<div>A fully online prior learning assessment and recognition (PLAR) tool for internationally educated nurses (IENs) was developed and tested by an inter-professional team at Ryerson University. The tool consisted of two stages: a self-assessment component followed by a multiple-choice examination and narrative (vignette) evaluation. The purposes of the study were to describe the demographic profile of the IEN registered nurse (RN), to develop the benchmark responses that demonstrate competency at the entry-to-practice level of the typical IEN RN, and to describe the experience of completing an online PLAR tool. A mixed-method approach was used. Findings demonstrated that IEN RNs who immigrate to Ontario, Canada, are of various ages and come from a wide spectrum of countries. The PLAR process holds promise for an objective assessment of IEN's eligibility to write the Canadian Registered Nurses Examination (CRNE) and to meet a global need. Further testing of the tool across a broader sample is required. (Contains 5 figures and 2 tables.)</div><div><br></div><div>ERIC</div>
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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 | 0.028 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".