Exploring Self-Efficacy and Anxiety in First-Year Nursing Students Enrolled in a Discipline-Specific Scholarly Writing Course
Bibliographic record
Abstract
Background: Very few studies measuring writing self-efficacy or anxiety in undergraduate nursing students exist in the education literature. The purpose of the present investigation was to identify if changes to writing self-efficacy and writing anxiety will occur in first-year baccalaureate nursing students who are exposed to a discipline-specific scholarly writing course employing scaffolding strategies as the primary instructional method. Concurrently, this study was the pilot test for a new measure assessing writing self-efficacy, The Self-Efficacy Scale for Academic Writing. Method: A one-group pre-test/posttest design was employed. Sixty-four (64) paired questionnaires were available for analysis. Bandura’s self-efficacy theory and a scaffolding process guided the study. Results: Anxiety was significantly reduced from pre-test to posttest (p = .005). Writing self-efficacy improved and was near but not significant (p = .051). Writing self-efficacy at pre-test predicted 15.4% of the variance in final self-reported grade on the scholarly paper (p = .001). Students who reported writing their paper late or last minute reported significantly higher writing self-efficacy compared to students who reported adhering to the paper task schedule (p = .021). There were no differences in writing self-efficacy scores based on student past experience with writing or their help seeking activities. Conclusion: First year nursing students can benefit from taking a discipline-specific writing course incorporating scaffolding as an instructional method as both writing anxiety and writing self-efficacy can potentially be improved in this population. However additional research is required to support this claim.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".