Using Simulation and Virtual Practice in Midwifery and Nursing Education: Experiencing Self-Body-World “Differently”
Bibliographic record
Abstract
The journey into the world of midwifery or nursing requires the student to attend to the intertwining of self-body-world in order to shift their knowledge of self-body-world into a client/patient-centered context. One of the teaching-learning strategies used to provide safe opportunities is the use of simulations and virtual practices. Rather than learning intimate acts of touching, or life and death decision-making in situations with actual clients/patients, students enter their learning world with rubber torsos, cloth babies, and cyber clinics. The “other” is a simulated other, not a human. How does the student shift from seeing this simulated other as object to a sense of other as subject? In our world of constant use of technology for communication and entertainment, do students shift in and out of a cyber world easily or are they more captured by the simulated experience than with the human world? Has the human world redefined itself where the intertwining of self-body-world blurs the sense of where human body ends and cyber or simulated world begins? What is the place of Bildung when engaged with a cyber other? As a result of educational challenges, including rising enrolments, limited clinical placement opportunities, and increasing risk management concerns, there has been a proliferation in the use of simulation as a teaching strategy (Fox, Damazo, 2013; Schmitt, Gilbert, Brandt, Weinstein, 2013). This has left us –the authors– wondering about the student experience of simulation. What do they learn? How do they learn? How can this learning be applied in practice?
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".