Challenges in Pharmacotherapeutics Education for Diabetes in Real-World Clinical Settings: Views From Family Medicine and Internal Medicine Residents
Bibliographic record
Abstract
PURPOSE: Pharmacotherapy for diabetes in real-world clinical settings is very complex and is posing a challenge for residents in training. The purpose of this study was to explore the views of residents in Canada regarding educational priorities for pharmacotherapy in diabetes management. METHODS: A questionnaire was developed to explore different domains of pharmacotherapy in diabetes management, including different clinic>al settings, combination pharmacotherapy with different classes of medications and patients' characteristics, including comorbidities and cardiovascular risk factors. The questionnaire and the letter of invitation was sent to residents through their program directors. The results were gathered through an online survey system. Due to the study design, response rate could not be determined. For data analysis, SPSS Software was used for statistical analysis. Chi-square testing was utilized for comparisons of proportions. RESULTS: Thirty-four residency programs in Canada were contacted and 165 residents completed the study. A significant number of the residents (59%) viewed combination pharmacotherapy for diabetes management as the most important educational priority (p < 0.001). Regarding insulin therapy, combination of insulin with another class of agents was recognized as the most important educational priority by 51% of residents (p < 0.001). Among all classes of medication for blood glucose management the education on the use of newer class of medication such as GLP1 agonists, DPP4 inhibitors and SGLT2 inhibitors was recognized as a priority by 77% of residents (p < 0.001). CONCLUSION: This study provides new data and insights into residents' views on diabetes pharmacotherapy. Educational curriculums may incorporate these views from residents on the educational priorities that were identified in this study.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".