Will Mobile Diabetes Education Teams (MDETs) in primary care improve patient care processes and health outcomes? Study protocol for a randomized controlled trial
Bibliographic record
Abstract
Background: There is evidence to suggest that delivery of diabetes self-management support by diabetes educators in primary care may improve patient care processes and patient clinical outcomes; however, the evaluation of such a model in primary care is nonexistent in Canada. This article describes the design for the evaluation of the implementation of Mobile Diabetes Education Teams (MDETs) in primary care settings in Canada. Methods/design: This study will use a non-blinded, cluster-randomized controlled trial stepped wedge design to evaluate the Mobile Diabetes Education Teams' intervention in improving patient clinical and care process outcomes. A total of 1,200 patient charts at participating primary care sites will be reviewed for data extraction. Eligible patients will be those aged >= 18, who have type 2 diabetes and a hemoglobin A1c (HbA1c) of >= 8%. Clusters (that is, primary care sites) will be randomized to the intervention and control group using a block randomization procedure within practice size as the blocking factor. A stepped wedge design will be used to sequentially roll out the intervention so that all clusters eventually receive the intervention. The time at which each cluster begins the intervention is randomized to one of the four roll out periods (0, 6, 12, and 18 months). Clusters that are randomized into the intervention later will act as the control for those receiving the intervention earlier. The primary outcome measure will be the difference in the proportion of patients who achieve the recommended HbA1c target of <= 7% between intervention and control groups. Qualitative work (in-depth interviews with primary care physicians, MDET educators and patients; and MDET educators' field notes and debriefing sessions) will be undertaken to assess the implementation process and effectiveness of the MDET intervention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.060 | 0.066 |
| Meta-epidemiology (narrow) | 0.007 | 0.005 |
| Meta-epidemiology (broad) | 0.015 | 0.007 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.115 | 0.017 |
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".