Reported self-efficacy of nursing clinical instructors in a bachelor’s of science in nursing program
Bibliographic record
Abstract
Clinical instructors play a significant role in the development of safe and competent nursing students. When nurses beginning their career as a clinical instructor, a substantial gap in knowledge can existence in the expectations of this new role. A deficiency of formal education in nursing education or orientation to this position can lead to a lack of self-efficacy and knowledge among clinical instructors. Research supports that a formal orientation and training increases feelings of self-efficacy among clinical instructors. The purpose of this study was to evaluate an online educational program on clinical instructor’s knowledge and self-efficacy towards teaching in a pre-licensure bachelors of science in nursing program. A pre-test/post-test design was utilized to assess changes in knowledge and self-efficacy immediately before and after the intervention for ten clinical instructors. Directly following the training, knowledge scores were measured with a statically significant result. In addition, immediately after the training and three months after the training, self-efficacy scores were measured and found to be statically significantly. In conclusion, the educational intervention was found to be statistically significant in improving the knowledge and self-efficacy scores among clinical instructors in the program as evidenced by the pre-test/post-tests results. This program was cost-effective to implement as there was no cost to the school of nursing or clinical instructors. The instructors could complete the online training from any location that had internet access and during any time of the day or night at their convenience.
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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 | 0.005 |
| 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) | 0.003 | 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".