International challenges without borders: a descriptive study of family physicians' educational needs in the field of diabetes
Bibliographic record
Abstract
BACKGROUND: The optimal care of persons with diabetes by general practitioners and family physicians (GP/FP) is complex and requires multiple competencies. This is a fairly unrecognized key challenge in the healthcare systems. In some cases, local and national Continuous Professional Development (CPD) initiatives target these challenges; however there have been few international initiatives, possibly because challenges emerging from different studies have not been linked across national boundaries. In this context, the authors have compiled data about gaps and/or barriers inherent to GP/FP care of persons with type 2 diabetes from Austria, Canada, Germany and the United Kingdom. METHODS: Secondary analyzes of pre-existing studies were conducted to identify challenges in the care of patients with type 2 diabetes as faced by GPs/FPs. Two sources of data were reviewed: unpublished research data from collaborating organizations and articles from a literature search (in English and German). Articles retrieved were scanned by the research team for relevance to the study objectives and to extract existing gaps and barriers. The identified challenges were then categorized along three major axes: (1) phase of the continuum of care {from screening to management}; (2) learning domain {knowledge, skills, attitudes, behavior, context}; and (3) by country/region. Compilation and categorization were performed by qualitative researchers and discrepancies were resolved through discussion until concordance was achieved. RESULTS AND DISCUSSION: Thirteen challenges faced by GPs/FPs in the care for patients with type 2 diabetes were common in at least 3 of the 4 targeted countries/regions. These issues were found across the entire continuum of care and included: pathophysiology of diabetes, diagnostic criteria, treatment targets assessment, drugs' modes of action, decision-making in therapies, treatment guidelines, insulin therapy, adherence, management of complications, lifestyle changes, team integration, bureaucracy and third-party payers. The issues reported were not restricted to the physicians' knowledge, but also related to their skills, attitudes, behaviours and context. CONCLUSIONS: This study revealed challenges faced by GPs/FPs when caring for patients with diabetes, which were similar across international and health system borders. Common issues might be addressed more efficiently through international educational designs, adapted to each country's healthcare system, helping develop and maintain physicians' competencies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".