Perceptions of preparedness for nursing practice using a preceptorship model
Bibliographic record
Abstract
Nursing graduates need to be “real world ready”, and able to meet the demands of the healthcare workforce. Research indicates that baccalaureate graduates have adequate theoretical base, but often lack competence in the clinical setting. Preceptorship programs are an effective way of developing clinical competence in the nursing student. The purpose of this study was to compare a traditional senior clinical course to a preceptorship model on students, faculty, and nurses’ perceptions of student preparedness for the nursing role. A formal preceptorship program with the support of a clinical nurse faculty member was developed to enhance the success of clinical nursing education. A quasi-experimental design with nonequivalent groups was used to determine the feasibility and effectiveness of a preceptorship model for senior nursing students comparing the students’, the faculty, and the nurses’ perceptions of the students’ preparedness for clinical practice after a traditional clinical and a preceptor clinical experience. The sample consisted of the fall 2017 senior semester cohort and the spring 2018 senior semester cohort, senior faculty who taught in those semesters, and nurses at the participating facilities. Overall, findings did not show a statistically significant difference between the traditional cohorts and the precepted cohorts; however, there is evidence of clinical significance. After implementation of the preceptorship model, there was an increase in the percent of nurses (100%), faculty (100%), and students (95%) who felt that the senior nursing students were ready for the professional role of a registered nurse.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".