Translation and Psychometric Evaluation of the Partners in Health Scale Among Iranian Adults With Chronic Diseases
Bibliographic record
Abstract
Objectives: Characterizing the psychometric attributes of the Persian variant of partners in health (PIH) in multiple sclerosis (MS), Diabetes, and Low Back Pain (LBP) patients. Methods: In this cross-sectional study, 183 MS, diabetes, and LBP patients (70 male, 113 female) were treated with PIH post-forward-backward translation. Confirmatory factor analysis was used for studying the factor structure. Cronbach’s α and McDonald’s Ω coefficients were used to analyze PIH internal consistency. We used an interclass correlation coefficient to evaluate test-retest reliability. Criterion validity was determined by studying the correlation of PIH and Short Form (36) Health Survey (SF-36), Diabetes Self-Management Scale (DSMS), and Self-Efficacy in Chronic Disease Self-Management (SES6G). Results: The median age of the participants was 49.73±15.16 years, 113 (61.75%) of them were female, 64 (35.0%) had MS, 66 (36.1%) had diabetes, and 53 (29.0%) had LBP. Content validity was determined across all areas (clarity, relevancy, simplicity) by a content validity index ≥0.82. Additionally, all items were confirmed via a content validity ratio ≥0.78. The outcome of CFA depicts that the statistics presented as model fit were as follows: CFI= 0.938, NFI= 0.899, and RMSEA= 0.085. All PIH items exhibited valid internal consistency (0.886-0.893). The PIH showed sufficient test-retest reliability regarding its corresponding subscales (0.554-0.679). The construct validity was confirmed by the total scores of PIH correlated with the total score of SF-36, SES6G, and DSMS. Discussion: The Persian variant of the PIH showed sufficient validity and reliability as a measure to assess self-management in patients suffering from chronic disease (MS, diabetes, and LBP).
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".