Evaluation of the Effect of a Computerized Training Simulator (ANAKIN) on the Retention of Neonatal Resuscitation Skills
Bibliographic record
Abstract
BACKGROUND: Neonatal resuscitation knowledge and skills deteriorate after initial training. PURPOSE: To evaluate the effectiveness of a computerized simulator system (ANAKIN) as a means for boosting neonatal resuscitation knowledge, skills, and self-reported confidence beliefs. METHOD: A randomized pretest-posttest control group study design involving 60 3rd-year medical students. At a 4-month, post-training interval, experimental group was exposed to ANAKIN and control group to a training video. Both groups assessed at an 8-month, post-neonatal resuscitation training interval. RESULTS: Knowledge level for both groups decreased significantly at 4- and 8-month, post-training intervals despite booster exposure. Confidence level for both study groups increased significantly following booster exposure. However, no significant difference between study group skill levels at 8 months and no significant relation between neonatal resuscitation knowledge, confidence, or skills. CONCLUSION: Computerized simulator system was as effective as video for maintaining resuscitation skills of medical students, and students were very satisfied with experience of remote computer simulation training.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".