The Role of Patient–Physician Symmetry in Influencing Diabetes-Related Distress Among Emerging Adults With Type 1 Diabetes
Bibliographic record
Abstract
Objective: Emerging adults (EA; individuals aged 18–29 years) with Type 1 Diabetes (T1D) are at risk for diabetes-related distress (DD), in part because of unique challenges associated with this developmental stage. Symmetry, or match between patient preferences and physician behavior during the medical encounter, is associated with better patient outcomes in adults with diabetes. Yet, the relationships between symmetry and patient outcomes are understudied among EA with T1D. This study examined whether DD differed as a function of patterns of patient–physician symmetry across three domains: information sharing (providing sufficient explanations), behavioral involvement (encouraging patient to take an active role in diabetes care), and socioemotional support (supporting well-being). Method: In total, 114 EA were recruited through a midwestern academic medical center or via social media. Participants self-reported demographic and disease information, DD, preferences for their endocrinologist’s behavior, and perceptions of their endocrinologist’s behavior via an online survey. Analyses of covariances examined whether DD differed across four symmetry groups (symmetrical high preference/high behavior, symmetrical low preference/low behavior, asymmetrical high preference/low behavior, and asymmetrical low preference/high behavior). Results: Asymmetry in behavioral involvement was related to higher DD in several domains. DD was highest among participants with a high preference for physician behavioral involvement but demonstrated low behavioral involvement. DD did not differ across symmetry groups for information sharing or socioemotional support. Conclusion: Future research should replicate these findings via larger, more diverse samples. Clinicians should consider matching patients to physicians based on preference for behavioral involvement.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".