From their eyes: Nursing student experiences using repeated reflection from the pediatric patient's perspective
Bibliographic record
Abstract
Simulation-based learning (SBL) has been a core course component in nursing curricula for decades. The growing use of SBL has led to increasingly lifelike simulations and continued development to maximize learning opportunities. Reflection and debriefing are key components of SBL to improve learning outcomes and clinical skills. Reflection is often described as a process; however, nursing students rarely have the opportunity to participate in the same simulation or clinical experience twice to completely engage in the reflective process. \nReflection from the patient’s perspective is a new concept to simulation, first done by Taplay (2020) using the Reflective Practice from the Patient’s Perspective (RPPP) tool. In this study, we applied the RPPP 3.0 tool to a pediatric nursing simulation, where the simulated child wore spyglasses to record visual and audio data of the simulation (Taplay, 2020). Participants watched their simulations from the patient’s perspective and partook in an interview guided by the RPPP 3.0 tool (Taplay, 2020). Then, participants returned within 2-9 days to repeat the same simulation and reflection. \nParticipants found value and meaning in the repeated reflection. Themes of reactions, communication, appraisal of performance, and the difference were found. Reflecting from the pediatric patient’s perspective allowed participants to gain insight into how their actions and communication were perceived. Repeating reflection encouraged participants to partake in self-directed preparation and allowed them to gain confidence, implement change, and improve their practice.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".