Reading and writing in nursing education
Bibliographic record
Abstract
Background and objective: Faculty identified the need for a gateway writing course (GWC) to prepare nurses for the writing requirements in the RN-to-BSN Completion (RNC) curriculum. This article describes the rationale for and development of a discipline-specific GWC developed for a RNC curriculum and reports research of student perspectives about the course and its effectiveness in preparing them to write in their nursing courses.Methods: The mixed method study included pre- and post-course surveys of self-efficacy in reading, writing, and research skills. Focus groups and interviews were used to identify readiness for and success in meeting curricular expectations for students who enrolled in the GWC and those who did not.Results: Statistically significant improvement occurred in all self-efficacy measures (reading: p ≤ .005, writing: p ≤ .01, accessing articles: p ≤ .005) from the beginning to the end of the GWC. Focus groups and interviews revealed five themes indicating improved readiness in students completing the GWC. Themes included perceptions of readiness, awareness, and preparedness for nursing courses; perceptions of confidence, mastery, efficiency, and self-sufficiency; enhanced knowledge of and ability to navigate academic processes and resourses; mastery of APA; and evidence of a reflective mindset and an evolving sense of professional identity.Conclusions: Students felt prepared for the RNC program after completing the GWC, and this sense of preparedness persisted throughout the program. The skills learned in the GWC allowed students to focus on understanding the professional concepts in nursing.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".