Proving My Competency One Test at a Time: Internationally Educated Nurses and the Canadian English Language Benchmark Assessment for Nurses
Bibliographic record
Abstract
Professional language assessment is a new concept that has great potential to benefit Internationally Educated Professionals and the communities they serve.This thesis reports on a qualitative study that examined the responses of 16 Canadian English Language Benchmark Assessment for Nurses (CELBAN) test-takers on the topic of their perceptions of the CELBAN test-taking experience in Ontario in the winter of 2015.An Ontario organization involved in registering participants distributed an e-mail through their listserv.Thematic analyses of focus group and interview transcripts identified 7 themes from the data.These themes were used to inform conclusions to the following questions: (1) How do IENs characterize their assessment experience?(2) How do IENs describe the testing constructs measured by the CELBAN?(3) What, if any, potential sources of construct irrelevant variance (CIV) do the test-takers describe based on their assessment experience?(4) Do IENs feel that the CELBAN tasks provide a good reflection of the types of communicative tasks required of a nurse?Overall, participants reported positive experiences with the CELBAN as an assessment of their language skills, and noted some instances in which they felt some factors external to the assessment impacted their demonstration of their knowledge and skill.Lastly, some test-takers noted the challenge of completing the CELBAN where the types of communicative nursing tasks included in the assessment differed from nursing tasks typical of an IENs country or origin.The findings are discussed in relation to literature on high-stakes large-scale assessment and IEPs, and a set of recommendations are offered to future CELBAN administration.These recommendations include (1) the provision of a webpage listing all licensure requirements (2) monitoring of CELBAN
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".