Evaluation of TeamSTEPPS integration across a curriculum regarding team attitudes: A longitudinal study
Bibliographic record
Abstract
Introduction : The ability to function as an effective member of the healthcare team is essential for graduate nurses upon entry into practice. Team training is therefore, an important element of nursing education. A convenient and cost-effective approach to providing team training to nursing students is through the TeamSTEPPS ® curriculum. The purpose of the study was to determine if an intentional integration of TeamSTEPPS ® principles into simulation-based team training modules would improve attitudes toward teamwork in a cohort of undergraduate nursing students. Methods : A quasi–experimental time series nonequivalent control group design was used. A convenience sample of 115 first semester students (108 completed) who received the TeamSTEPPS ® training and 77 final semester undergraduate students who did not receive the intervention participated. Repeated measures of the TeamSTEPPS-Teamwork Attitude Questionnaire (T-TAQ) were obtained initially and three times throughout the curriculum. Final semester students served as the comparison group and completed the T-TAQ without formal team training. Results : After participation in ten hours of simulation-based instructional activities, T-TAQ scores significantly increased from baseline and maintained over time. No statistical difference was identified between first semester students without formal team training and graduating students without formal team training. Conclusions : The findings suggest an intentional integration of TeamSTEPPS ® principles throughout an undergraduate-nursing curriculum improve and maintain student teamwork attitudes over time. It is recommended that TeamSTEPPS ® principles be intentionally integrated throughout undergraduate nursing curricula.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.001 | 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".