Evaluation of a unique and innovative diabetes care model in primary care in Ontario, Canada: protocol for a multiple-methods study with a convergent parallel design
Bibliographic record
Abstract
INTRODUCTION: The growth and complexity of diabetes are exceeding the capacity of family physicians, resulting in the demand for community-based, interprofessional, primary care-led transition clinics. The Primary Care Diabetes Support Programme (PCDSP) in London, Ontario, is an innovative approach to diabetes care for high-risk populations, such as medically or socially complex and unattached patients. In this study, we will employ a quadruple-aim approach to evaluate the health system impacts of the PCDSP. METHODS AND ANALYSIS: We will use multiple methods through a convergent parallel design in this project across five unique studies: a case study, a patient study, a provider study, a complications study and a cost-effectiveness study. The project will be conducted in a dedicated stand-alone clinic specialising in chronic disease management, specifically focusing on diabetes care. Participants will include clinic staff, administrators, family physicians, specialists and patients with type 1 or type 2 diabetes who received care at the clinic between 2011 and 2023. The project design will define the intervention, support replication at other sites or for other chronic diseases and address each of the quadruple aims and equity. Following the execution of the five individual studies, we will build a business case by integrating the results. Data will be analysed using both qualitative (content analysis and thematic analysis) and quantitative techniques (descriptive statistics and multiple logistic regression). ETHICS AND DISSEMINATION: We received approval from the research ethics boards at Western University (reference ID: 2023-1 21 766; 2023-1 22 326) and Lawson Health Research Institute (reference ID: R-23-202). A privacy review was completed by St. Joseph's Healthcare Corporation. The findings will be shared among PCDSP staff and patients, stakeholders, academic researchers and the public through stakeholder sessions, conferences, peer-reviewed publications, infographics, posters, media interviews, social media and online discussions. For the patient and provider study, all participants will be asked to provide consent and are free to withdraw from the study, without penalty, until the data are combined. Participants will not be identified in any report or presentation except in the case study, for which, given the number of PCDSP providers, we will seek explicit consent to identify them.
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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 | 0.090 | 0.056 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.028 | 0.004 |
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".