Psychological predictors of health-related quality of life of interstitial lung disease patients
Bibliographic record
Abstract
Introduction: Previous studies with Interstitial Lung Disease (ILD) patients have been found that the quality of life of the patient diminishes in function of the severity of symptoms, as well as the presence of anxious and depressive symptomatology. Nevertheless, it hasn’t been explored if there’s an association between quality of life and perceived psychological stress which patients are on a daily basis. Purpose: To investigate the association between perceived psychological stress and health related quality of life of patients with ILD. Method: A cross-sectional study was realized. 38 patients with ILD were included. The psychological assessment was made with the Perceived Stress Scale, Hospital Anxiety and Depression Scale, Montreal Cognitive Assessment (MOCA), and SF-12 Health Survey. It was built a multiple regression model to estimate the influence of the variables on the quality of life, on SPSS V25 software. Results: Fifty-nine percent of the sample was male. The mean age was 65.03±10.07 years. Multiple regression found that only depression (t=-2.53, p = 016, β = -.322) and perceived stress (t=-4-48, p = .000, β = -.494) had a significate effect on health-related quality of life [F (4, 34,) = 33.52, p<.001, R 2 = .77]; explaining 77% of the total variance, regardless of the sex and age factors. Conclusion: Further than the disease symptomatology, the depression symptoms and the perceived psychological stress are important factors that deteriorate the quality of life of the ILD patient. Thus, it’s important to provide interdisciplinary care that addresses such factors to improve the quality of life of the ILD patients.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".