Designers Corner - Capturing Day-to-Day Aspects of Living with Chronic Illness: The Need for Longitudinal Designs
Bibliographic record
Abstract
see, my time is occupied trying to live. ..There are those adjustments that you have to make if you're going to cope with it at all... Somehow things work out. But to say they get easier is far from the truth. They get harder. Because it's harder this year for me to even get around... It's so tremendous a frustration that I don't even think of it as a frustration. I mean, I can't explain it... But I don't sit around saying I'm frustrated I don't think. Eut I know life is one huge mountain of frustration. I think it's so big that you can't, you can't, ah, you just can't talk about it. It's hard to know what is the worst part. Because it's from the time you open your eyes in the morning until you close them at night. Your frustration never stops. And 1 think that's what gets ya. I can't sit and cry about it. Sometimes I wish I could... You get to the end of your tether some days and I wish sometimes I could just sit down and get a little release from it. But you can't. That's the worst part of it. Its omnipresence, you know. It never ceases. This quote from an elderly woman who has advanced macular degeneration and is caring for a spouse who has advanced Parkinson's disease (Russell, 1994) reveals how her current life's work focuses on their illnesses. She experiences the unremitting omnipresence of chronic illness as it pervades every aspect of her life, from the moment she wakes up in the morning until the moment she falls asleep at night. However, it is not the dramatic and unexpected situations that she is referring to. Her discourse is about her ordinary, daily routine of living with chronic illness, the taken-for-granted everyday activities of one who is chronically ill.
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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 | 0.354 | 0.417 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".