Perspectives and experiences of health care professionals and patients regarding treatments for type 2 diabetes
Bibliographic record
Abstract
BACKGROUND: Several treatment options exist for type 2 diabetes, but little is known about the factors considered by health care providers (HCPs) and patients in Canada in making therapeutic decisions. This study explores perceptions and practices of HCPs and patients related to add-on (i.e., second-line) therapy for type 2 diabetes when initial therapy no longer provides adequate glycemic control. METHODS: HCPs (pharmacists, family physicians, diabetes educators, endocrinologists and nurse practitioners) and patients with type 2 diabetes in Ottawa and Halifax were randomly selected to participate in the study. Phone interviews were conducted with endocrinologists and nurse practitioners and focus groups with the other HCPs and patients. RESULTS AND INTERPRETATION: Sixty HCPs and 14 patients participated in the study. Metformin was consistently reported by prescribers (physicians and nurse practitioners) as the preferred initial therapy. Important factors in choosing second-line therapy (once glycemic control was inadequate with metformin) were antihyperglycemic efficacy, risk of hypoglycemia and weight gain, and long-term safety. Other considerations were cost, insurance coverage and patient preference. There were differences within and between HCP groups in how these other factors were considered and in the perceived advantages and disadvantages of each drug class. Some patients expressed anxiety when second-line agents were prescribed, and others felt poorly informed about treatment options. CONCLUSION: In choosing a second-line therapy for type 2 diabetes, most HCPs placed a high priority on antihyperglycemic efficacy, although there was considerable variability in the relative weight placed on other factors. These findings point to an opportunity for pharmacists to collaborate more actively with other HCPs to ensure that treatment decisions are based on the best available evidence and to educate and involve patients in these decisions.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".