“Better Caught than Taught”: A Study Exploring Habits of Nursing Student Literacy
Bibliographic record
Abstract
Literacy is complex and impacts safety in nursing. When using language, nurses are constantly switching between medical language proficiency, academic language proficiency, and social language proficiency. The purpose of this mixed-methods study was to trial an English language exam specific to the nursing profession – the Canadian English Language Benchmark Assessment for Nurses (CELBAN) – in 13 nursing students and identify how language testing scores related to habits of literacy in reading, writing, speaking, and listening. In addition to writing the CELBAN exam students competed several questionnaires and participated in a one-on-one interview where they discussed their literacy habits and completed a series of literacy activities including C-tests and role plays. Findings indicated that the students’ performance on the CELBAN and C-test was related. Integrated data collection showed that students with low reading and writing self-efficacies and poor reading habits tended to also have poorer performance on the CELBAN and C-tests. Presently, medical language literacy is a facet of nursing practice that is “better caught than taught.” Overall, this study adds to the literature by demonstrating that students have reading, writing, speaking and listening habits that are amenable to intervention through attention to pedagogical practices such as simulated learning and formalized supports.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".