Nursing Students’ Academic Success Factors
Bibliographic record
Abstract
BACKGROUND: Attrition from nursing programs is an ongoing concern. Capitalizing on nursing students' strengths and supporting areas for remediation will maximize student success. PURPOSE: This study explored undergraduate nursing student strengths and areas for remediation at program entry and across all years of nursing education study. METHODS: We used a cross-sectional design and collected data via the Academic Success Inventory for College Students survey tool. Baseline data were collected on first-year students after program start, and data were collected for all years of study at the end of the academic terms. RESULTS: Compared with other undergraduate students, nursing students exhibited strengths in study skills, in self-organization strategies, in their certainty of progress toward career goals, in recognizing the importance of their studies, and in levels of socializing that did not hinder academic performance. At some data collection points, they had strengths in motivation, confidence, and concentration. Nursing students indicated areas for remediation in studying or test-taking anxiety and their perception of the educator's ability to organize, teach, and assess student progress. CONCLUSION: Nurse educators' pedagogical approaches should augment nursing student strengths. Remediation is required to support student success relative to anxiety, and students need orientation to the process of learning.
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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 | 0.010 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".