Prioritizing Illness: Lessons in Self-Managing Multiple Chronic Diseases
Bibliographic record
Abstract
Chronic disease management strategies are largely based on single disease models, yet patients often need to manage multiple conditions. This study uses the concepts of ‘chronic illness trajectory’ and ‘biographical disruption’ to examine how patients self-manage multiple chronic conditions and especially how they prioritize which condition(s) will receive the greatest attention. Fifty-three people with multiple chronic illnesses participated in one of 6 focus groups. The results suggest that people who were disrupted tended to be younger than 60, lived on their own, cared for other family members, or other barriers. Many participants anticipated subsequent illnesses given their age and prior experience with illness. In order to cope with their multiple illnesses most felt it was necessary to prioritize their ‘main’ illness. Their reasons for prioritizing a particular illness included: (1) the unpredictable nature of the disease; (2) the condition could not be controlled by tablets; and (3) the condition tended to set off the rest of their health problems. Social context played a key role in shaping patients’ biography and chronic illness trajectory.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.006 | 0.014 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".