Video recording of neonatal resuscitation: A feasibility study to inform widespread adoption
Bibliographic record
Abstract
AIM: To determine the feasibility of introducing video recording (VR) of neonatal resuscitation (NR) in a perinatal centre. METHODS: This was a prospective cohort quality improvement study on preterm infants and their caregivers. Based on evidence and experience of other centers using VR intervention, a contextually relevant implementation and evaluation strategy was designed in the planning phase. The components of intervention were pre-resuscitation team huddle, VR of NR and video debriefing (VD), all occurring on the same day. Various domains of feasibility and sustainability as well as feasibility criteria were predefined. Data for analysis was collected using quantitative and qualitative methods. RESULTS: Seventy-one caregivers participated in VD of 14 NRs facilitated by six trained instructors. Ninety-one percent of caregivers perceived enhanced learning and patient safety and, 48 issues were identified related to policy, caregiver roles, and latent safety threats. Ninety percent of caregivers expressed their willingness to participate in VD activity and supported the idea of integrating it into a resuscitation team routine. Eighty-three percent and 50% of instructors expressed satisfaction with video review software and quality of audio VR. No issues about maintenance of infant or caregivers' confidentiality and erasure of videos were reported. Criteria for feasibility were met (refusal rate of < 10%, VR performed on > 50% of occasions, and < 20% caregivers' perceiving a negative impact on team performance). Necessary adaptations to enhance sustainability were identified. CONCLUSION: VR of NR as a standard of care quality assurance activity to enhance caregivers' learning and create opportunities that improve patient safety is feasible. Despite its complexity with inherent challenges in implementation, the intervention was acceptable, implementable, and potentially sustainable with adaptations.
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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 | 0.038 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".