Designing technologies for self-care: Describing the lived experiences of individuals with rheumatoid arthritis
Bibliographic record
Abstract
Self-care of a chronic illness is a lifelong management process that includes taking medications, monitoring symptoms, and coping with emotional and lifestyle changes. Human-Computer Interaction (HCI) research has often responded to these needs by developing technologies that help an individual quantify aspects of their chronic illness, like daily pain, flare ups, or personal behaviours like diet and exercise. But this quantification fails to account for the ongoing needs of understanding ones disease and maintaining a balanced lifestyle. To understand these needs, we interviewed 12 people about their lived experience with Rheumatoid Arthritis (RA). We performed a thematic analysis of collected data and identified three types of support currently lacking for RA: (1) psychosocial care (2) patient agency, and (3) lifestyle adaptations. Our results highlight the need to support long term uncertainty while living with a chronic illness and identify needs the HCI community should consider when developing self-care technologies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".