Canadian Family Medicine residents' clinical training in diabetes mellitus
Bibliographic record
Abstract
ProblemThe College of Family Physicians ofCanada (CFPC) has identified diabetes as one of the top five priority topics for assessment ofcompetence in residency training, and has established evaluation objectives to clearly describe the domain ofcompetence that should be tested for managing diabetes.Unfortunately, little is known as to whether Canadian Family Medicine Residency programs are providing adequate training to residents in managing diabetes. MethodsA multi-method selfreported survey approach (paper and electronic) was utilized for this study to determine the level ofdiabetes experience received by graduating family medicine residents in Canada.Eleven Family Medicine departments provided permission to survey graduating residents (5 in Westem Canada and 6 in Eastem Canada).All subjects were surveyed using the electronic questionnaire, and residents in Vy'estem Canada received an intensified survey approach using both electronic and paper methods. ResultsThe final response rate was 30.56% (n=136).Residents within the intensified survey approach had a significantly higher response rate (X2:30.108;1df;p<.001)thanresidents who only received the electronic survey method.Results demonstrated that residents did not receive adequate training in the areas of diabetes management cited within the CFPC evaluation objectives.Training was considered most adequate in diagnosing ofdiabetes, however results showed that 94% of respondents had little to no experience in diagnosing Type 1 diabetes.Training in diabetes medications noted that 7 4%;o ofrespondents had little to no experience in initiating insulin, and routines that were initiated were representative oftraditional approaches (sliding scales and BID routines).Residents noted that training in initiating oral anti-hyperglycemic agents was adequate in only 40% ofrespondents, with exposure occurring only within first line therapies.Finally, clinical experience in managing acute diabetes complications was low for hypoglycemia (68.7% of respondents), DKA (64.2o/o of respondents), and HHNS (88.0% of respondents). ConclusionsResidency programs need to improve diabetes training to ensure that residents ¡eceive the key features identified by the CFPC as essential for competent management ofdiabetes in clinical situations.Acknowledqements I would personally like to acknowledge the Manitoba Medical Service Foundation (MMSF) as a key contributor to this project.The MMSF has been a strong supporter of health-related research and education in Manitoba, and through their review process chose to embrace this project as an important educational initiative.Without their financial assistance, the completion of this project would not have been possible.I would also like to ack¡owledge the guidance and support that was provided by my thesis committee through the evolution of this research project.Projects such as this are signifìcant leaming processes, and valuable insight was imparted to me from my committee in key areas of the ¡esearch design, methodology, data collection and analysis, and ove¡all interpretation and writing up of the final results, I appreciated the constructive appraisal throughout the course of this project.My situation was unique to many students, as geographical limitations significantly reduced the ability to meet on a face to face basis with my committee, I appreciate the flexibility that was shown to continue communication and teaching tluough altemative means such as email and phone discussions.I would like to acknowledge and ofler a special
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".