Development and Validation of the McGill Empowerment Assessment–Diabetes (MEA-D)
Bibliographic record
Abstract
BACKGROUND: Diabetes is a prevalent chronic condition that poses a major burden for patients and the health care system. Evidence suggests that patient engagement in self-care improves diabetes control and reduces the risk of complications. To provide effective interventions that aim to improve empowerment processes relating to diabetes, a comprehensive and valid measure of empowerment is needed. This article details the development and validation of the McGill Empowerment Assessment-Diabetes (MEA-D). METHODS: The development and validation of the MEA-D questionnaire comprised three steps: item generation, qualitative face validation, and factorial content validation. An initial version was created by combining existing items and inductively generated items. Items were mapped to an empowerment framework with four domains: attitude, knowledge, behavior, and relatedness. Semi-structured interviews were conducted with 21 adults living with diabetes to assess face validity. The questionnaire was revised by a team of clinicians, researchers, and patient-partners. Factorial content validation was then performed using responses from 300 adult Canadians living with type 1 or type 2 diabetes. RESULTS: The final version of the MEA-D contained 28 items. A moderately good item-domain correlation was found between the individual items within the four domains. Cronbach's α was 0.81 (95% CI 0.78-0.85) for attitude, 0.73 (95% CI 0.67-0.79) for knowledge, 0.84 (95% CI 0.81-0.87) for behavior, and 0.81 (95% CI 0.77-0.84) for relatedness. CONCLUSION: The evaluation of diabetes programs demands a validated measure of empowerment. We developed the MEA-D to address this need. The MEA-D may be adapted to measure patients' empowerment regarding other chronic health conditions.
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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 | 0.022 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".