Obstetric Safety: The Security Apparatus Enhanced the Self-Efficiency of Medical Students in Vaginal Birth Practice in a Simulation Trial
Bibliographic record
Abstract
Currently, the safety of patients is an integral part of clinical practice, especially within medical schools. The safety device and the environment had to be concerned when medical education modules were set up. One of the most worriers in obstetrical practice among undergraduates was vaginal birth training. The inadequate safety instrument in training made students loss of their self-reliance and competence. This study aimed to test the effect of a new safety apparatus on the self-confidence and clinical performance of undergraduates on vaginal birth training. The medical students were randomized to this sample and split into two groups for two vaginal birth simulation stations; convention and intervention. The participants’ self-confidence assessment was carried out at the end of trial. In addition, clinical performance ratings on vaginal birth simulation were analyzed by experts during the experiments. There was 40 medical students attended to this trial and found a significant statistical increment in GSE and CPAT scores in the intervention trial. All volunteers were satisfied with the new safety equipment and more confident to taking care of mothers in vaginal birth practices. We concluded that this innovation could boost the confidence of medical students in vaginal birth practices and increase their clinical performance in simulation. However, it needs to be checked again in the workplace.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.007 | 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".