Challenges faced by physicians when discussing the Type 2 diabetes diagnosis with patients: insights from a cross‐national study (IntroDia<sup>®</sup>)
Bibliographic record
Abstract
AIMS: To investigate physicians' recalled experiences of their conversations with patients at diagnosis of Type 2 diabetes, because physician-patient communication at that time may influence the patient's subsequent self-care and outcomes. METHODS: ), we conducted a cross-sectional survey of physicians treating people with Type 2 diabetes in 26 countries across Africa, Asia, Europe, Latin America, the Middle East, North America and Oceania. The survey battery was designed to evaluate physician experiences during diagnosis conversations as well as physician empathy (measured using the Jefferson Scale of Physician Empathy). RESULTS: survey (response rate 73.0%). Most respondents (87.5%) agreed that the conversation at diagnosis of Type 2 diabetes impacts the patient's acceptance of the condition and self-care. However, almost all physicians (98.9%) reported challenges during this conversation. Exploratory factor analysis revealed two related yet distinct types of challenges (r = 0.64, P < 0.0001) associated with either patients (eight challenges, α = 0.87) or the situation itself at diagnosis (four challenges, α = 0.72). There was a significant inverse association between physician empathy and overall challenge burden, as well as between empathy and each of the two types of challenges (all P < 0.0001). Study limitations include reliance on accurate physician recall and inability to assign causality to observed associations. CONCLUSIONS: Globally, most physicians indicated that conversations with patients at diagnosis of Type 2 diabetes strongly influence patient self-care. Higher physician empathy was associated with fewer challenges during the diagnosis conversation.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".