Reliability and validity of the Chinese version of the LMC Skills, Confidence & Preparedness Index (SCPI) in patients with type 2 diabetes
Bibliographic record
Abstract
Abstract Background A variety of diabetes self-management instruments have been developed but few of them consist of the preparedness for diabetes self-management behavior. The novel psychometric evaluation tool “the LMC Skills, Confidence & Preparedness Index (SCPI)” measures three key aspects of a patient’s diabetes self-management: knowledge of the skill, confidence in being able to perform skill and preparedness to implement the skill. The objective of this study was to translate, adapt and validate the SCPI for use in Chinese adult patients with type 2 diabetes. Methods This study followed the guideline recommended by the American Academy of Orthopaedic Surgeons Evidence Based Medicine Committee (AAOS) to indigenize the scale. Forward and back translation, and cross-cultural language debugging were completed according to the recommended steps. A convenience sample of Chinese patients with type 2 diabetes (n = 375) were recruited from a university-affiliated hospital in Shanghai. The validity (criterion, discriminant validity, and construct validity), reliability (internal consistency and test–retest reliability) and the interpretability of the instrument were examined. The content validity was calculated by experts’ evaluation. Results The Chinese version of SCPI (C-SCPI) has good internal consistency with a Cronbach’s alpha of 0.92. The ceiling effects of the preparedness subscales is 21%. The criterion validity of three dimensions of C-SCPI was established with significantly moderate correlations between the DKT, DES-SF and SDSCA (p < 0.05). The S-CVI of the whole scale was 0.83. Except for entry 21, the I-CVI values of all entries were greater than 0.78. The C-SCPI has also shown good discriminative validity with statistically significant differences between the patients with good and poor glycemic control. Confirmatory factor analysis showed that modified results indicate that the fitting degree of the model is good, χ2/df = 2.775, RMSEA = 0.069, CFI = 0.903, GFI = 0.873, TLI = 0.889, IFI = 0.904. The test–retest reliability coefficient was 0.61 (p < 0.01). Conclusion We established a Chinese version of SCPI through translation and cross-cultural adaptation. The C-SCPI is reliable and valid for assessment of the level of self-management in Chinese patients with type 2 diabetes.
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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 | 0.008 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".