Privacy and Trust in Healthcare IoT Data Sharing: A Snapshot of the Users’ Perspectives (Preprint)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<sec> <title>BACKGROUND</title> Healthcare services in Canada are slowly shifting from in-hospital care to patient-centred, home-care services. Collecting and sharing personal data from individuals via Internet of Things (IoT) devices has become a critical part of this change, which can lead to better decision-making and better support for patients from healthcare providers. However, some challenges come from using technology, including concerns around trust in organizations holding individuals' data and privacy and security related to data sharing that needs to be considered as part of this new model of care. </sec> <sec> <title>OBJECTIVE</title> This study investigates users' trust in sharing their data collected using healthcare IoT devices via different organizations. </sec> <sec> <title>METHODS</title> This research project leveraged a literature review and online questionnaires to understand how general users of IoT for Health perceive and trust different types of organizations (large companies, government, healthcare providers, and insurance companies). A total of 400 participants were recruited using Mechanical Turk for the online questionnaire, using a between- subjects design. Each participant was presented with a scenario related to using various IoT technologies, information about data sharing, and a list of privacy concerns associated with specific organizations that handle health-related data. Based on this scenario, participants were asked to answer 16 trust-related questions. Results were analyzed using Analysis of Variance (ANOVA), followed by posthoc comparisons using the pairwise t-test with the Bonferroni correction. </sec> <sec> <title>RESULTS</title> The study showed no significant differences regarding privacy concerns (LConcern) in Canada, the United States (USA), and Europe (F (2, 389) = 0.736, P = .480). Overall levels of trust (Ltrust) in the USA varied significantly between large companies, government, healthcare providers, and insurance companies (F (3, 388) = 10.107, P < .05). The same results were observed in Canada, with a significant difference between the four types of organizations (F (3, 125) = 6.882, P < .05), USA (F (3, 128) = 4.488, P =.05), and in Europe, as well (F (3, 127) = 4.451, P < 0.05). </sec> <sec> <title>CONCLUSIONS</title> The results suggest differences in users' perceptions of trust associated with the types of organizations. Additionally, levels of concern regarding privacy and data ownership varied among users. The findings identified differences in the perception of trust between the different regions of the participants. </sec>
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.006 |
| 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 it