Comparison of second-degree and traditional baccalaureate nursing students’ performance in managing acute patient deterioration events
Bibliographic record
Abstract
Background: Students in accelerated second-degree programs are reported to be highly motivated, older, competitive, maintain higher grade point averages than their traditional counterparts, and score higher on standardized nursing achievement tests. However, studies that directly measure clinical performance parameters of students in accelerated second-degree programs in direct side-by-side comparison with traditional students under similarly controlled conditions have not been reported. Aim: The purpose of this study was to compare traditional and second-degree baccalaureate nursing students’ performance of key assessments and interventions in the management of deteriorating patients in a simulated task environment. Methods: A convenience sample of 20 traditional and 20 accelerated undergraduate baccalaureate-nursing students participated. The four high-fidelity simulation exercises required the participants to detect early signs of patient deterioration and initiate treatment-based interventions. Two research personnel independently coded audio and videotaped data. The coders recorded the first time in which an assessment or intervention was performed. An independent samples t -test was performed to determine differences in nursing students’ performance of key assessments and interventions. Results: Second-degree accelerated nursing students were in general more likely to recognize and respond to indicators of patient deterioration more promptly than their traditional counterparts. Conclusions: Second-degree students appear to possess attributes that increase the likelihood that they will appreciate stimuli in the clinical environment, which is a precursor to effective intervention. Further research is required to substantiate the factors that account for performance differences between these traditional and second-degree baccalaureate nursing students.
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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 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".