Videoconferencing can be used to assess neonatal resuscitation skills
Bibliographic record
Abstract
OBJECTIVE: To examine the hypothesis that an instructor in a remote site can accurately assess the practical skills of a provider performing a simulated neonatal resuscitation (megacode) using videoconferencing. METHODS: Using volunteer NRP providers and instructors, two local telemedicine sites were linked using six telephone lines. Camera angles, sound settings and equipment placement were optimized. Instructors tested providers at the other site. Instructors recorded their observations on checklists based on those of the Neonatal Resuscitation Program (NRP), and all participants completed feedback forms and gave verbal feedback. Based on the results of the pilot study, the protocol and recording tools were developed, and providers at a rural centre were tested. Tests were carried out with local and remote instructors. Observations of local and remote instructors were collected independently, and compared. Opinions of providers were also collected. RESULTS: Observations of the local and remote instructors on the performances of the providers were consistent; 15 of 18 megacodes reached the required standard. Six telephone lines were required for transmission without noticeable delay in sound transmission. Viewing quality was sufficient for remote instructors to provide feedback on ventilation technique. Providers indicated that videoconferencing did not interfere with their performance and would willingly repeat the experience. Cronbach's alpha for assessment of the technical features was 0.80 or greater for all groups. CONCLUSIONS: Videoconferencing can be used to test resuscitation providers in remote centres. It can enhance neonatal resuscitation education in areas where experienced instructors are in short supply.
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".