Supporting graduate students’ skills with simulated experiences in a professional foundation course
Bibliographic record
Abstract
The population’s health depends on a well-educated nursing workforce that includes graduate-prepared nurses. However, the nation’s demand for graduate nurses in advanced practice, teaching, and research roles surpasses the supply. Graduate nursing educators can support student success by creating positive learning experiences for students at the beginning of their study programs. Initially, we created a pilot writing orientation, which was implemented for new students. Although the results of the pilot study showed significance with paired t-tests (p < .000; Cohen’s d = 1.21), the writing skills were not applied in long-term coursework. Therefore, a seven-week course was created to provide students with small-scale assignments, preparing them for complex future graduate coursework. The study aimed to evaluate the effectiveness of the skills learned in the course using student (n = 15) and faculty (n = 9) focus groups. A qualitative design using thematic analysis showed the following student themes: tools to improve scholarly writing, magnitude and feasibility of the project, graduate-level writing and professional presentations, clear expectations of achieving program requirements, and professional development using a digital e-portfolio. Faculty focus groups compared the skills of students who had completed the course and those who had not. Faculty themes included stronger writing skills despite the variation, resource availability, tool use, APA format skills, and writing synthesis skills development. Overall, the participants’ perspectives shared positive feedback with insightful suggestions for future course improvement.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".