People With Haemophilia as Data Coordinators: An Analysis of the Ethics and Feasibility of Self‐Management With Personal Health Records
Bibliographic record
Abstract
BACKGROUND: People with haemophilia perform various self-management tasks, supported by multiple health apps. Personal health records will enable individuals to access and add health information from different institutions in a single digital tool, providing an integrated overview of data. Later, individuals will also be able to share their data with health care providers and relatives. This creates a new role for users: Coordinator of data exchange. OBJECTIVE: To analyze if and how personal health records contribute to self-management, with a particular emphasis on the role of coordinating data exchange. METHODS: We applied various interpretations of self-management to the promises of personal health records to identify what goals it intends to achieve. We then assessed various skills and responsibilities that are required from users to work with personal health records. Last, we analyzed potential scenarios of the coordination of data exchange. RESULTS: Personal health records promise to support both compliant self-management (i.e., managing care according to medical regimens) and concordant self-management (i.e., managing care according to personal values and goals). Which of these forms is promoted depends on the goal of data coordinating tasks. The chosen design of the data sharing feature may impact the usability and accessibility of personal health records for a wide group of users. CONCLUSION: What form of self-management is promoted by personal health records needs to be more clearly defined. A participatory design strategy can ensure that the design of coordinating data exchange matches individuals' and health care providers' needs.
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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 | 0.048 | 0.126 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".