Optimizing Diabetes Self-management Using the Novel Skills, Confidence, and Preparedness Index (SCPI)
Bibliographic record
Abstract
OBJECTIVE The Skills, Confidence, and Preparedness Index (SCPI) is an electronic tool designed to assess three dimensions (knowledge, confidence, and preparedness) in a clinically relevant measure with immediate feedback to guide the individualization of patient education. This study sought to assess the validity and reliability of the final SCPI generation, its relevance to glycemia, and its responsiveness to patient education. RESEARCH DESIGN AND METHODS In Part 1, patients with type 1 and type 2 diabetes were recruited from specialist clinics over a 6-month period and completed the 23-item SCPI using a tablet. In Part 2, participants also underwent a diabetes self-management education (DSME) program. Baseline SCPI score was used to guide the DSME, and SCPI and glycemia were assessed at completion. RESULTS In total, 423 patients met inclusion criteria and 405 had evaluable data. SCPI scores were found to have a high degree of validity, internal consistency, and test-retest reliability, with no floor or ceiling effects. Scoring was negatively correlated with HbA1c (type 1 diabetes: r = −0.26, P = 0.001; type 2 diabetes: r = −0.20, P = 0.004). In 51 participants who underwent a DSME intervention (6.4 ± 0.6 visits over a mean ± SD 3.4 ± 0.8 months), mean HbA1c improvement was 1.2 ± 0.2% (13.1 ± 2.2 mmol/mol, P < 0.0001). Total SCPI score and each subscore improved in parallel. CONCLUSIONS The SCPI tool is a quick and easy-to-use measurement of three domains: skills, confidence, and preparedness. The instant scoring and feedback and its relationship to glycemic control should improve the efficiency and quality of individualizing care in the diabetes clinic.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".