Characteristics, Engagement and Academic Performance of First-Year Nursing Students in Selected Ontario Universities
Bibliographic record
Abstract
With an aging population nationally, nursing programs have struggled to meet the demand for nurses in our healthcare system. Student attrition remains high at 28% within the first two years of the Baccalaureate nursing programs. In order to meet healthcare system demand, nursing programs need to ensure that students persist, graduate, and are academically successful on the national examination. As a first step in student success, one needs to identify effective educational practices in first-year nursing programs that are associated with student engagement within the Canadian context. Extensive research in the U.S. has examined educational practices and student engagement. However, few national or international studies examined nursing student characteristics and engagement and student success. This study examined the extent to which first-year nursing students are engaged in effective educational practices and any relationships between student demographic, external, academic, social, and institutional variables, and student engagement. A descriptive correlational design was used to conduct a secondary analysis of pre-existing 2008 National Survey of Student Engagement (NSSE) data from nursing students in 13 Ontario Universities. Descriptive statistics were computed to examine student characteristics and the distribution of NSSE benchmark scores. Step-wise multiple regression analysis was used to identify relationships between predictor variables and student engagement and academic performance (grade point average). The results identified several significant predictors of first-year nursing student engagement including age, ethnicity, hours spent preparing for class per week, grade point average, hours per week spent participating in cocurricular activities, participating in physical fitness activities, and institutional size. Being a first-generation student and age were significant predictors of academic performance for first-year nursing students. The findings provide insight into some of the drivers of engagement in first-year nursing education and may also inform policy and practice for improving nursing student engagement and, ultimately, graduation rates.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".