Streaming Physiological Data: General Public Perceptions of Secondary Use and Application to Research in Neonatal Intensive Care
Bibliographic record
Abstract
High speed physiological data represents one of the most untapped resources in healthcare today and is a form of Big Data. Physiological data is captured and displayed on a wide range of devices in healthcare environments. Frequently this data is transitory and lost once initially displayed. Researchers wish to store and analyze these datasets, however, there is little evidence of any engagement with citizens regarding their perceptions of physiological data capture for secondary use. This paper presents the findings of a self-administered household survey (n=165, response rate = 34%) that investigated Australian and Canadian citizens' perceptions of such physiological data capture and re-use. Results indicate general public support for the secondary use of physiological streaming data. Discussion considers the potential application of such data in neonatal intensive care contexts in relation to our Artemis research. Consideration of the perceptions of secondary use of the streaming data as early as possible will assist in building appropriate use models, with a focus on parents in the neonatal context.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".