Translating Research into Practice: Impact of an Interprofessional Diabetes Education Model on Patient Health Outcomes
Bibliographic record
Abstract
Background: Patient education programs encouraging diabetes self-management can improve clinical outcomes and lessen diabetes complications. This study implemented an innovative interprofessional student-led diabetes self-management and health promotion program for an underserved population and demonstrated an improvement in participant clinical outcomes and students’ understanding of interprofessional aspects of diabetes care.Methods and Findings: This community-based program was implemented at two sites that serve medically underserved individuals. Students from five health career professions led educational sessions designed to demonstrate critical components of diabetes self-management. The six-month longitudinal program covered topics within the Alphabet Strategy, including Advice, Blood pressure, Cholesterol, Diabetes control, Dental care, Diet, Eye care, Foot care, and Guardian drugs. Participants completed surveys evaluating diabetes knowledge, understanding of diabetes care, and health behaviours. Clinical values were collected before and after the program. Student surveys assessed their understanding of diabetes self-management. Upon completion of the program, all assessments were repeated to determine if there were improvements in outcomes. Thirty-eight participants and thirty students completed the study. There were significant improvements in participants’ diabetes knowledge, understanding of diabetes management, and clinical outcomes. There were significant improvements in the students’ ability to educate patients about foot care, eye care, and guardian drugs, as well as increased awareness of the role of each health profession in diabetes care.Conclusions: This interprofessional health promotion model showed significant improvements in patient and student outcomes. This innovative student-led program could be implemented in other settings and for the management of other chronic diseases.
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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 | 0.040 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| 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".