Continuous Glucose Monitoring User-Wear Experience Fosters Empathy and Learning
Bibliographic record
Abstract
OBJECTIVE: To determine if a continuous glucose monitoring (CGM) user-wear experience brings value to an advanced diabetes elective course by assessing the impact on empathy and knowledge. METHODS: This was a quasi-experimental pre-post intervention study, conducted over 2 offerings of an advanced diabetes elective course. Third-year pharmacy students participated in a 2-part didactic education and user-wear experience involving CGM devices. Students completed a survey at 3 prespecified time points to assess empathy and knowledge (foundational and counseling knowledge). Empathy was assessed using the Kiersma-Chen empathy scale. Knowledge was assessed using predefined multiple-choice questions. Statistical tests include repeated measures analysis of variance and Bonferroni tests for overall and subsection scores on the empathy and knowledge surveys. A partial eta squared was also used to measure effect size for the repeated measures analysis of variance test. RESULTS: Twenty-nine out of the 36 enrolled students completed all 3 surveys. Compared with a traditional lecture, the CGM user-wear experience demonstrated a significant increase in student self-perceived empathy and counseling knowledge. No change in foundational knowledge was observed. CONCLUSION: A CGM user-wear experience provides educational value beyond a traditional lecture. Our study showed that educational outcomes such as empathy and counseling knowledge can be achieved by implementing a CGM user-wear experience. An advanced diabetes elective course provides an ideal environment to optimize CGM learning outcomes with a user-wear experience.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".