The diabetes management experiences questionnaire: Psychometric validation among adults with type 1 diabetes
Bibliographic record
Abstract
AbstractAimsTo examine the psychometric properties of the Diabetes Management Experiences Questionnaire (DME‐Q). Adapted from the validated Glucose Monitoring Experiences Questionnaire, the DME‐Q captures satisfaction with diabetes management irrespective of treatment modalities.MethodsThe DME‐Q was completed by adults with type 1 diabetes as part of a randomized controlled trial comparing hybrid closed loop (HCL) to standard therapy. Most psychometric properties were examined with pre‐randomization data (n = 149); responsiveness was examined using baseline and 26‐week follow‐up data (n = 120).ResultsPre‐randomization, participants' mean age was 44 ± 12 years, 52% were women. HbA1c was 61 ± 11 mmol/mol (7.8 ± 1.0%), diabetes duration was 24 ± 12 years and 47% used an insulin pump prior to the trial. A forced three‐factor analysis revealed three expected domains, that is, ‘Convenience’, ‘Effectiveness’ and ‘Intrusiveness’, and a forced one‐factor solution was also satisfactory. Internal consistency reliability was strong for the three subscales ( range = 0.74–0.84) and ‘Total satisfaction’ = 0.85). Convergent validity was demonstrated with moderate correlations between DME‐Q ‘Total satisfaction’ and diabetes distress (PAID: rs = −0.57) and treatment satisfaction (DTSQ; rs = 0.58). Divergent validity was demonstrated with a weak correlation with prospective/retrospective memory (PRMQ: rs = −0.16 and − 0.13 respectively). Responsiveness was demonstrated, as participants randomized to HCL had higher ‘Effectiveness’ and ‘Total satisfaction’ scores than those randomized to standard therapy.ConclusionsThe 22‐item DME‐Q is a brief, acceptable, reliable measure with satisfactory structural and construct validity, which is responsive to intervention. The DME‐Q is likely to be useful for evaluation of new pharmaceutical agents and technologies in research and clinical settings.
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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 | 0.005 | 0.008 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".