Structure of Lifestyle Disruptions in Chronic Disease
Bibliographic record
Abstract
BACKGROUND: The Illness Intrusiveness Ratings Scale (IIRS) measures the extent to which disease or its treatment or both interfere with activities in important life domains. Before comparing IIRS scores within or across groups it is crucial to determine whether a common underlying factor structure exists across patient populations. OBJECTIVE: To investigate the factor structure underlying the IIRS and evaluate its stability across diagnoses. METHODS: IIRS responses from 5,671 respondents were pooled from 15 separate studies concerning quality of life in eight patient groups: rheumatoid arthritis; osteoarthritis; systemic lupus erythematosus; multiple sclerosis; end-stage renal disease (maintenance dialysis); renal transplantation; heart, liver, and lung transplantation; and insomnia. Data were gathered by different methods (eg, interview, self-administered, mail survey) and in diverse contexts (eg, individual vs. group). RESULTS: Exploratory maximum-likelihood factor analysis identified three underlying factors in a randomly selected subset of respondents (n = 400), corresponding to "Relationships and Personal Development," "Intimacy," and "Instrumental" life domains. Confirmatory factor analysis corroborated the stability of this structure in an independent subsample (n = 2100). Complementary goodness-of-fit indices confirmed the consistency of the three-factor solution, corroborating that IIRS scores are uniquely defined across patient populations. Coefficient alpha was high for total and subscale scores. CONCLUSIONS: IIRS scores can be compared meaningfully within and across patient groups. Both total and subscale scores can be used depending on research objectives.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".