Evaluation on simulation training for midwifery science trainers in Windhoek, Namibia
Bibliographic record
Abstract
Simulation training refines skills needed to correct mistakes by allowing trainees to fine tune their skills. To improve the knowledge and skills of the midwifery educators, a simulation training has been organised for them so that they are able to provide simulation to the students. This was in part necessitated by the understanding/observation that students tend to be less anxious at the clinical practice after they had simulation training. A quantitative, cross-sectional study design was employed. Data was collected with structured self-administered questionnaires among 10 midwifery trainers who attended a simulation training workshop. This workshop was conducted by trainers from Cardiff University under the Phoenix project in June 2016. Due to the limited number of trained educators, census sampling method was used and data was analysed using SPSS version 25. The study results indicated that most midwifery educators are female within middle age category. Most participants have attended simulation training before and have been conducting simulation to students. Furthermore, most participants indicated that they were satisfied with the training and that they gained knowledge and skills on simulation that they can utilize during student training. The researchers recommend further research on evaluation of knowledge and skills such as evaluating participants on simulating procedures.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".