Living with chronic illness scale: international validation of a new self-report measure in Parkinson’s disease
Bibliographic record
Abstract
Abstract Understanding how a person lives with a chronic illness, such as Parkinson’s disease (PD), is necessary to provide individualized care and professionals role in person-centered care at clinical and community levels is paramount. The present study was aimed to analyze the psychometric properties of the Living with Chronic Illness-PD Scale (EC-PC) in a wide Spanish-speaking population with PD. International cross-sectional study with retest was carried out with 324 patients from four Latin American countries and Spain. Feasibility, acceptability, scaling assumptions, reliability, precision, and construct validity were tested. The study included 324 patients, with age (mean±s.d.) 66.67±10.68 years. None of the EC-PC items had missing values and all acceptability parameters fulfilled the standard criteria. Around two-third of the items (61.54%) met scaling assumptions standards. Concerning internal consistency, Cronbach’s alpha values were 0.68–0.88; item-total correlation was >0.30, except for two items; item homogeneity index was >0.30, and inter-item correlation values 0.14–0.76. Intraclass correlation coefficient for EC-PC stability was 0.76 and standard error of measurement (s.e.m.) for precision was 8.60 (for a EC-PC s.d.=18.57). EC-PC presented strong correlation with social support (rS=0.61) and moderate correlation with life satisfaction (rS=0.46). Weak and negligible correlations were found with the other scales. Internal validity correlations ranged from 0.46 to 0.78. EC-PC total scores were significantly different for each severity level based on Hoehn and Yahr and Clinical Impression of Severity Index, but not for Patient Global Impression of Severity. The EC-PC has satisfactory acceptability, reliability, precision, and validity to evaluate living with PD.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".