Internet Protocol Television for Personalized Home-Based Health Information: Design-Based Research on a Diabetes Education System
Bibliographic record
Abstract
BACKGROUND: The use of Internet protocol television (IPTV) as a channel for consumer health information is a relatively under-explored area of medical Internet research. IPTV may afford new opportunities for health care service providers to provide health information and for consumers, patients, and caretakers to access health information. The technologies of Web 2.0 add a new and even less explored dimension to IPTV's potential. OBJECTIVE: Our research explored an application of Web 2.0 integrated with IPTV for personalized home-based health information in diabetes education, particularly for people with diabetes who are not strong computer and Internet users, and thus may miss out on Web-based resources. We wanted to establish whether this system could enable diabetes educators to deliver personalized health information directly to people with diabetes in their homes; and whether this system could encourage people with diabetes who make little use of Web-based health information to build their health literacy via the interface of a home television screen and remote control. METHODS: This project was undertaken as design-based research in two stages. Stage 1 comprised a feasibility study into the technical work required to integrate an existing Web 2.0 platform with an existing IPTV system, populated with content and implemented for user trials in a laboratory setting. Stage 2 comprised an evaluation of the system by consumers and providers of diabetes information. RESULTS: The project succeeded in developing a Web 2.0 IPTV system for people with diabetes and low literacies and their diabetes educators. The performance of the system in the laboratory setting gave them the confidence to engage seriously in thinking about the actual and potential features and benefits of a more widely-implemented system. In their feedback they pointed out a range of critical usability and usefulness issues related to Web 2.0 affordances and learning fundamentals. They also described their experiences with the system in terms that bode well for its educational potential, and they suggested many constructive improvements to the system. CONCLUSIONS: The integration of Web 2.0 and IPTV merits further technical development, business modeling, and health services and health outcomes research, as a solution to extend the reach and scale of home-based health care.
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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 | 0.019 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".