Assessing the Psychometric Properties of the Persian Version of the Adaptation to Chronic Illness Scale for Patients With Cardiovascular Disease
Bibliographic record
Abstract
Objective Patients with cardiovascular diseases (CVDs) often face numerous psychosocial challenges and emotional fluctuations due to their chronic conditions. Their ability to adapt to the disease and manage the emotional, psychological, and social challenges is crucial for these individuals. This study aimed to assess the psychometric characteristics of the Persian version of the adaptation to chronic illness scale (ACIS) for patients with CVDs in Iran. Materials & Methods This is a cross-sectional and psychometrics study. The forward-backward translation method was first used to translate the ACIS. To assess its face validity, it was administered to 10 CVD patients and reviewed by five experts in counseling and psychology. The content validity index (CVI) and the content validity ratio (CVR) were calculated to assess content validity. Concurrent validity was evaluated by measuring the correlation between the scores of the Persian ACIS and the psychosocial adjustment to illness scale (PAIS). Internal consistency was assessed using Cronbach’s α coefficient, and test re-test reliability was measured using the intraclass correlation coefficient (ICC). Confirmatory factor analysis (CFA) was performed in LISREL software to validate the structure of the questionnaire. Results The CVR was obtained as 0.99, the item-CVI ranged from 0.8 to 1, and the calculated kappa coefficient ranged from 0.76 to 1. There was a strong correlation between the total scores of the ACIS and PAIS (r=-0.757, P<0.05). The Cronbach’s α coefficient for the overall scale was 0.84. Furthermore, the ICC was 0.96. The values of fit indices for the initial model were RMSEA=0.104, df=3.04, GFI=0.75, AGFI=0.70, and NFI=0.72. After removing item 10 (in the physical subscale) due to its lack of significance, and calculating the covariation of errors for paired items (22 & 23, 13 & 15, 17 & 19, 12 & 20, and 6 & 25), the values of fit indices improved as RMSEA=0.077, df=2.23, GFI=0.90, AGFI=0.91, and NFI=0.90, reaching acceptable construct validity. Conclusion The Persian ACIS has acceptable face and content validity, concurrent validity, internal consistency, and test re-test reliability. The results also confirmed its three-factor structure with 24 items for CVDs. Thus, it can be utilized in research and clinical settings in Iran for patients with CVDs.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".