Technology preferences and routines for sharing health information during the treatment of a chronic illness
Bibliographic record
Abstract
When a patient has a chronic illness, such as heart disease or cancer, it can be challenging for distributed family members to stay aware of the patient's health status. A variety of technologies are available to support health information sharing (e.g., phone, video chat, social media), yet we still do not have a detailed understanding of which technologies are preferred and what challenges people still face when sharing information with them. To explore this, we conducted a mixed-method study-involving a survey and in-depth interviews--with people about their health information sharing routines and preferences for different technologies. Regardless of physical distance between distributed family members, synchronous methods of communication afforded the opportunity to provide affective support while asynchronous methods of communication were deemed to be the least intrusive. With family members adopting certain roles during the treatment of chronic illnesses, our findings suggest the need to design tools that mediate sharing health information across distance and age gaps, with consideration to respecting patient privacy while sharing health information.
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".