Parental attitudes to digital recording: A paediatric hospital survey
Bibliographic record
Abstract
AIM: Digital recording is ubiquitous in the community. Its objectivity, permanence and utility in medical education have led to increasing use in health-care settings. As participants in this process, the perspectives of families are important to inform practice. We surveyed family members of hospitalized children to evaluate their opinions. METHODS: A survey was administered to adults in emergency, operating room or ICU waiting areas at a university-affiliated paediatric hospital in Toronto. Respondents rated the frequency of digital recording in the community and hospital environments, the acceptability of five clinical indications and of consent discussions. RESULTS: Participants completed 154 surveys (response rate 83%) with median (interquartile range) of 2 (1-2) children. Community use of recording >4 times in the week prior was reported by 47 (31%); 42 (28%) reported no recording. The respondents rated the following indications for digital recording acceptable in the health care research 142 (94%), medical education 140 (93%), quality improvement 140 (92%), patient safety 147 (97%), and clinical care (96%). Within healthcare, consent discussions at different times were rated as acceptable before recording by 99%; after recording by 41%; and with no consent by 17%. CONCLUSION: We performed the first post-privacy legislation survey of digital recording in Canadian health care. There is widespread acceptance of digital recording in public spaces and health care; however, respondents preferred to provide consent before recording. Balancing these preferences with the demonstrated advantages of video recording in health care presents challenges for optimal health policy creation. This study provides contemporary data to inform discussions.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".