Prevalence and indications for video recording in the health care setting in North American and British paediatric hospitals
Bibliographic record
Abstract
BACKGROUND: Health care video recording has demonstrated value in education, performance assessment, quality improvement and clinical care. METHODS: A survey was administered to paediatric hospitals in Great Britain, Canada and the United States. Heads of departments or delegates from six areas (emergency departments [EDs], operating rooms, paediatric intensive care units [PICUs], neonatal intensive care units [NICUs], simulation centres and neuroepilepsy units) were asked 10 questions about the prevalence, indications and process issues of video recording. RESULTS: Seventy hospitals were surveyed, totalling 307 clinical areas. The hospital response rate was 100%; the rate for clinical departments was 65%. Sixty-six hospitals (94%) currently use video recording. Video recording was used in 62 of 68 (91%) operating rooms; 36 of 69 (52%) PICUs; 35 of 67 (52%) NICUs; 12 of 65 (19%) EDs; seven of eight (88%) neuroepilepsy units and 13 of 14 (93%) simulation centres. Education was the most common indication (112 of 204 [55%]). Most sites obtained written consent. Since the introduction of more strict privacy legislation, 11 of 65 (17%) EDs have discontinued video recording. CONCLUSION: The present study describes video recording practices in paediatric hospitals in North America and Great Britain. Video recording is primarily used for education and most areas have a consent process.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".