Evaluation of Glycemic Index Education in People Living with Type 2 Diabetes: Participant Satisfaction, Knowledge Uptake, and Application
Bibliographic record
Abstract
The glycemic index (GI) has been included in the Canadian clinical practice guidelines for type 2 diabetes (T2D) management since 2003, and even longer in other parts of the world (e.g., Australia). Despite this, dietitians have reported that GI is “too difficult for patients to understand and apply.” They have called for diverse GI-utility data and evidence-informed education materials. To address these concerns, we developed and evaluated a GI education workshop and supporting materials, using the Kirkpatrick Model, for a T2D population. Participants (n = 29) with T2D attended a dietitian-facilitated workshop and received education materials. A mixed-form questionnaire (GIQ) and 3-day-diet-record were used to capture patient demographics, satisfaction, knowledge, and application, prior to and immediately after the workshop, 1-week, and 4-weeks post-education. Dietary GI was significantly lower at 1 and 4 weeks post-education (mean ± SEM; both 54 ± 1), compared to pre-education (58 ± 1; p ≤ 0.001). Participants (28/29) were satisfied with the intervention. The GI knowledge score was significantly higher post-education at baseline (83.5 ± 3.4%; p ≤ 0.001), week one (87.5 ± 2.6%; p = 0.035), and week four (87.6 ± 3.8%; p = 0.011) when compared to pre-education (53.6 ± 5.1%). A significant reduction in dietary GI was achieved by participants living with T2D, after completing the workshop, and they were able to acquire and apply GI knowledge in a relatively short period.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".