Development of Newborn and Umbilical Cord in A Low-cost Model for Teaching and Training
Bibliographic record
Abstract
Each year, nursing schools spend more on classroom supplies at the nursing lab to help nursing students develop their practical skills before clinical internships. To teach nursing students how to prepare for their experience in the clinic, the nursing school needs to purchase a variety of teaching tools. The objective of this study was to create a low-cost model of a newborn and umbilical cord so that nursing students could practice cutting severing newborns' umbilical cords in the nursing laboratory until they were proficient before entering the delivery department to care for newborns. The newborn and umbilical cord in a low-cost model was created in association with the nursing and engineering faculties. We created the differences between the original newborn model and the newborn and umbilical cord in a low-cost model: The newborn and umbilical cord in a low-cost model can move its limbs and use the remote to control the operation, in contrast to the original newborn model which can't move and doesn't cry. Data were collected from expert interviews and newborn and umbilical cord model tests using an assessment form. Data were analyzed using Descriptive statistics and content analysis. The newborn and umbilical cord model is made from a hose that is cheaper and simple to cut, it is like a real umbilical cord so we can make our mannequins at a very low cost and effective. Newborn and umbilical cord in a low-cost model can be used in practice. Reduce the cost of ordering teaching supplies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".