23. An Internal Audit of a Virtual Learning Space to Facilitate Clinical Decision-Making in Nursing
Bibliographic record
Abstract
In any nursing program, it is a challenge to foster an awareness of, and engagement with, the complexity and reality of nursing practice. During their studies, nursing students have to learn the relevant underpinning theoretical knowledge for practice as well as develop their understanding of the role and responsibilities of the registered nurse in clinical settings. At a regional Australian university the Bachelor of Nursing is offered externally with the student cohort predominantly off-campus. There are significant challenges in providing opportunities to enhance learning (Henderson, Twentyman, Heel, & Lloyd, 2006) and to foster early professional engagement with the nursing community of practice (Andrew, McGuiness, Reid, & Corcoran, 2009; Elliot, Efron, Wright, & Martinelli, 2003; Morales-Mann & Kaitell, 2001) in a context for learning nursing knowledge and inter-professional collaborative practice. This paper presents the results of a series of internal audits of students’ feedback of the Charles Darwin Hospital (CDU) vHospital™ undertaken from 2008 to 2010, following integration into theory and clinical nursing subjects in the Bachelor of Nursing program. The feedback from students demonstrates the value students place on teaching and learning activities that provide realistic situated learning opportunities (Hercelinskyj & McEwan, 2011).
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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 | 0.024 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".