Selected abstracts from the 24th Annual Meeting of the Society in Europe for the Simulation Applied to Medicine. Debriefing with team deliberate practice: an instructional design to enhance the performance of undergraduate nursing students in recognising the deteriorating patient.
Bibliographic record
Abstract
Introduction & Aims \nThe purpose of this paper is to give an overview of the development of debriefing with team deliberate practice (DwTDP) as an innovative instructional design. The author will present their research findings and discuss its application to healthcare education. \n \nA key competency that undergraduate nurses have to achieve is that of the early recognition of clinical deterioration of patients the Nursing and Midwifery Council, and as advocated by the Chief Medical Officer simulation-based education is an important methodology in achieving this. Despite a growing evidence base for the use of simulation as a learning and teaching methodology Anderson and colleagues found a wide variation in the quality of delivery and recommended further research into those instructional design features that enhance learning. To meet this challenge, the author developed an innovative simulation-based educational enhancement entitled DwTDP. This was based on the deliberate practice framework and incorporated key elements from team working and debriefing theory. \n \nMethods \n \nUsing a quasi-experimental longitudinal pre-post design, the researcher explored the effect of DwTDP on the performance of second year adult nursing students (N = 93) over a one-year period. Naturally occurring student groups were randomised into either the intervention arm (n = 8), who received DwTDP, or the comparison arm (n = 8) who received a traditional SBE. \n \nResults & Discussion \n \nPre and post video data of the students’ performance was captured and collected at three time points over the course of the year. This was analyzed using a series of statistical techniques. An Independent t-test found that there was no statistically significant effect on the participant’s performance during the individual phases. However, a mixed ANOVA analysis identified that over time the DwTDP intervention led to a statistically significant improvement in the performance of the participants (F(1, 6) = 19.12, p = .005). \n \nIn phase 1 the participant’s time on task from the intervention group also showed statistical improvement (t(14) = 5.12, p<.001), with a very large effect size (d = 2.56). Although the effect sizes remained large in the other two phases the Independent t-tests were not statistically significant. \n \nThe results inferred that the DwTDP intervention was a feasible approach to use within nurse education. It enhanced the participant’s performance in recognising a deteriorating patient overtime and initially improved their response times. The author therefore recommends the use of this approach within adult nursing pre-registration curricula and further research into its efficacy with other healthcare professionals.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.171 | 0.050 |
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".