Empowering health care consumers in the era of Internet of Things
Bibliographic record
Abstract
Abstract The Internet of Things (IoT) is emerging as a significant development in information technology that aims to link the digital world with the real world to improve human life. IoT refers to digital tools collecting data and providing hyper‐personalized information to its users. With the rapid integration of the IoT in the healthcare sector (HIoT), it has been presumed that HIoT devices have an empowering effect on patients; however, this has yet to be investigated. Furthermore, the literature reveals a lack of consistency regarding the definition of patient empowerment. This study aims to fill these gaps and investigates whether HIoT systems increase user empowerment for individuals suffering from chronic illnesses. It also examines how empowerment is defined for HIoT users. To answer these two research questions, we conducted a qualitative research study consisting of 20 semi‐structured, in‐depth interviews carried out with individuals suffering from Type 1 diabetes (T1D). The interviews were transcribed and content analysis was conducted on the data. The study enabled us to examine whether and how the HIoT triggered empowerment for patients suffering from T1D. Findings reveal four main dimensions of empowerment for HIoT users: (1) self‐efficacy, (2) patient control, (3) knowledge development and (4) participation in the decision‐making process along with the doctor. Results also highlight that participants feel empowered by personal acceptance of living with their health condition and social support. In addition, the analysis led to the identification of the barriers which need to be overcome to ensure that HloT systems improve patient empowerment.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".