Internationally Educated Nurses and the Canadian English Language Benchmark Assessment for Nurses: A Qualitative Test Validation Study of Test-Taker Accounts
Bibliographic record
Abstract
This qualitative validation study examines sixteen Internationally Educated Nurses’ (IENs’) accounts of the Canadian English Language Benchmark Assessment for Nurses (CELBAN) at two testing centres (Toronto and Hamilton). This study adopts both focus groups and one-on-one interviews to investigate the inferences drawn from the test, and its consequences. Focus groups and interviews were conducted using an adapted interview guide utilized in the TOEFL iBT investigation of test-taker accounts of construct representation and construct irrelevant variance (DeLuca et al., 2013). While construct representation describes the degree of authenticity in the presentation of Canadian English language nursing tasks, construct irrelevant variance refers to potential factors impacting the test-taking experience which might contribute to a score variance that was not reflective of test-taker knowledge of the testing constructs (Messick, 1989, 1991, 1996). In this study, test-taker accounts of construct representation and construct irrelevant variance constituted the data which were coded and analyzed abductively via the sensitizing concepts derived from DeLuca et al., and Cheng and DeLuca (2011) on examining test-takers’ experience and their contribution to validity. Seven themes emerged, answering four research questions: How do IENs characterize their test experience? How do IENs describe the assessment constructs? What, if any, sources of Construct Irrelevant Variance (CIV) do IENs describe? Do IENs feel the language tasks are authentic? Overall, participants reported positive experiences with the CELBAN, while identifying some possible sources of CIV. Given the CELBAN’s widespread use for high-stakes decisions (a component of nursing certification and licensure), further research of IEN-test-taker responses to construct representation and construct irrelevant variance will remain critical to our understanding of the role of language competency testing for IENs.
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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 | 0.044 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.015 | 0.020 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| 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".