A comparison of face-to-face versus remote assessment of neonatal resuscitation skills
Bibliographic record
Abstract
The neonatal resuscitation skills of 30 third-year medical students were assessed in real time by a face-to-face examiner in the same room as the student, and by a remote examiner located in a separate room using the ANAKIN system. The ANAKIN system combines an instrumented manikin simulator, computer-based assessment and high-bandwidth videoconferencing. The students were assessed while performing a neonatal resuscitation megacode using the ANAKIN system. Students were satisfied with the ANAKIN system as an assessment system and were not intimidated by its use. However, the correlation between the face-to-face and remote examiner's mean total performance assessment scores was 0.27, which was not significant (P=0.14). The results indicated variation between the examiners' performance scores in a number of key technical skill areas. The findings from this study have implications for the use of technology-mediated systems in assessing resuscitation skills. Examiner orientation is critical for individuals using such systems. These persons must be comfortable and confident in using the technology. Interface and design features of the system need to be carefully scrutinized and tested.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".