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Record W4409889786 · doi:10.32598/rj.26.1.3465.3

Assessing the Psychometric Properties of the Persian Version of the Adaptation to Chronic Illness Scale for Patients With Cardiovascular Disease

2025· article· en· W4409889786 on OpenAlexaff
Mohammad Saeed Khanjani, Farzaneh Ebrahimgol, Manoochehr Azkhosh, Samaneh Hoseinzadeh, M Latifian, Sahar Esmaeili

Bibliographic record

VenueJournal of Rehabilitation · 2025
Typearticle
Languageen
FieldPsychology
TopicHealth and Well-being Studies
Canadian institutionsUniversity of Calgary
FundersUniversity of Social Welfare and Rehabilitation Sciences
KeywordsDiseaseScale (ratio)Adaptation (eye)Illness behaviorPersianMedicinePsychometricsClinical psychologyPsychiatryPhysical therapyPsychologyInternal medicine

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.266
Threshold uncertainty score0.189

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.302
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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