Toward a Service Design Method for Telehealth Personalization.
Bibliographic record
Abstract
Personalizing telehealth services in a manner that accounts for patients' preferences and interaction abilities could significantly improve patient adherence to telehealth treatment plans. We propose a service design method anchored in the concept of Value-in-Use (SerViU) to achieve such personalization. SerViU focuses on the level of patients’ personal service encounters within a telehealth service to support a continuous Use-Assess-Personalize process throughout the treatment duration. SerViU guides the decision-making about the personalization of a telehealth service by integrating an existing framework of information communication technology (ICT) service personalization that identifies three dimensions of personalization: architectural, relational, and technological. This research contributes to Health-IT research by providing a method that guides transforming standardized telehealth services into personalized services. This research also contributes to the service design research by differentiating between standard and personal service encounter levels, which is paramount to better supporting the personalization of ICT-enabled services.
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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 | 0.016 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".