Facilitators and barriers to SGLT2i and GLP1a prescribing in Northern Ontario: a qualitative interview study
Bibliographic record
Abstract
ABSTRACT Background One in three adults in Ontario, Canada has type 2 diabetes, obesity, heart failure, or chronic kidney disease, and the prevalence is even higher in Northern Ontario. Sodium glucose co-transporter 2 inhibitors (SGLT2i) and glucagon-like peptide-1 analogues (GLP1a) are highly effective medications to treat these conditions, but prescribing rates in Northern Ontario are low. This study aimed to explore the facilitators and barriers to SGLT2i and GLP1a prescribing for adults living with and without diabetes in Northern Ontario. Methods We conducted virtual, semistructured interviews of clinicians (i.e., physicians, nurse practitioners, resident physicians) working in Northern Ontario, Canada between July 2024 and November 2024. Interview transcripts were thematically coded into categories based on the Theoretical Domains Framework (TDF). Findings were classified as either barriers or facilitators, and then grouped to identify major subthemes within the data. Subthemes were then further aggregated into themes and mapped onto the Capability, Opportunity, Motivation-Behaviour (COM-B) model for behaviour change. Results We interviewed 25 clinicians, including eight physicians, eight resident physicians, and nine nurse practitioners caring for adults in Northern Ontario. Twenty-two of the interviews were held one-on-one and one was held as a co-interview with three participants. We identified five main barriers and five main facilitators to SGLT2i and GLP1a prescribing. The major barriers included: limited access to medications, patient challenges and competing demands, lack of familiarity, clinical identity, and prescribing inertia. Limited access to medications was a prominent theme with nested subthemes of high cost of medications for patients and insufficient compassionate drug programs to cover these costs. The major facilitators included: role as a clinician that follows the data, belief that SGLT2i/GLP1a use will improve patient outcomes, clinicians’ perceptions of patient openness to these drugs, comfort prescribing, and system and colleague supports. Conclusion Our findings provide useful insights to inform knowledge translation initiatives aimed at increasing the uptake of SGLT2i and GLP1a in Northern Ontario.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".