Perceptions of clinical competence among nurse pregraduates: Do different types of nursing programs make a difference?
Bibliographic record
Abstract
Background: New graduate nurses’ competence rarely meets the rigorous expectations of clinical settings. Researchers suggest that competency validation before graduation can shorten the length of clinical orientations for graduate nurses after they enter the workforce. The purpose of this study was to explore the difference in clinical competence between students in different types of nursing programs and to identify skills that need to be reinforced. Methods: This longitudinal study included 461 students from three different nursing programs: a four-year regular nursing program and a two-year RN-BSN program in day school and night school. A total of 478 students were invited to participate, and 440 students completed and returned questionnaires either at one year before or at the time of graduation with a total response rate of 92.05%. Results: At the time of graduation, students in all three nursing programs perceived low competence in overall clinical skills. Students in a two-year RN-BSN night school perceived significantly lower competence than students in two other types of nursing programs. Nurse students’ general performance skills and advanced nursing skills need to be reinforced before graduation. Conclusions: More opportunities for students’ involvement in case-based studies to cultivate their ability to integrate their knowledge and skills and more accumulated hours in deliberate practice with external sources such as electronic resources, training facilities and skill consultation on performance are recommended to enhance students’ clinical competence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".