Student self-confidence with clinical nursing competencies in a high-dose simulation clinical teaching model
Bibliographic record
Abstract
Objective: This paper describes undergraduate nursing students’ assessment of confidence in clinical practice within a modelthat uses a “high-dose” of clinical simulation to replace 50% of the traditional clinical experience hours in an upper division bachelor’s degree program. We assessed changes in self-reported confidence between the middle and end of a two-year nursingcurriculum. Design: Longitudinal design. We surveyed undergraduate nursing students to assess their perceived self-confidence in carryingout eight core competencies associated with generalist nursing practice with the Assessment of Nursing Education Scale (Robert Wood Johnson Foundation, 2009) at the mid-point (semester 2) and end of program (semester 4). Methods: Data were analyzed Generalized Linear models. To account for changes over time, we included program track(traditional BSN or 15-month accelerated second degree program) and gender (male/female) as co-variates in the models. Results: One hundred and twenty-two students completed the ANE at the two time points. Results for analysis of student confidence over time showed significant improvement on each of the eight domains of generalist nursing practice. There was nosignificant effect of gender or program type on student’s perceived self-confidence. Conclusions: Overall significant improvement in students’ self-assessed confidence from program mid-point to end-point lends support to the efficacy of a clinical teaching model that uses a high dose of simulation to substitute for traditional clinical hours.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".