Validation of the Self‐Efficacy for Managing Chronic Disease Scale: A Scleroderma Patient‐Centered Intervention Network Cohort Study
Bibliographic record
Abstract
OBJECTIVE: Self-management programs for patients with chronic illnesses, including rheumatic diseases, seek to enhance self-efficacy for performing health management behaviors. No measure of self-efficacy has been validated for patients with systemic sclerosis (SSc; scleroderma). The objective of this study was to assess the validity and internal consistency reliability of the Self-Efficacy for Managing Chronic Disease (SEMCD) scale in SSc. METHODS: English-speaking SSc patients enrolled in the Scleroderma Patient-centered Intervention Network Cohort who completed the SEMCD scale at their baseline assessment between March 2014 and June 2015 were included. Patients were enrolled from 21 sites in Canada, the US, and the UK. Confirmatory factor analysis (CFA) was used to evaluate the factor structure of the SEMCD scale. Cronbach's alpha was calculated to assess internal consistency reliability. Hypotheses on the direction and magnitude of Pearson's correlations with psychological and physical outcome measures were formulated and tested to examine convergent validity. RESULTS: A total of 553 patients were included. CFA supported the single-factor structure of the SEMCD scale (Tucker Lewis Index = 0.99, comparative fit index = 0.99, root mean square error of approximation = 0.10). Internal consistency was high (α = 0.93), and correlations with measures of psychological and physical functioning were moderate to large (|r| = 0.48-0.67, P < 0.001), confirming study hypotheses. CONCLUSION: Scores from the SEMCD scale are valid for measuring self-efficacy in patients with SSc, and results support using the scale as an outcome measure to evaluate the effectiveness of self-management programs in SSc.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.001 |
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".