Resolving Disease Management Problems in European‐American and Latino Couples with Type 2 Diabetes: The Effects of Ethnicity and Patient Gender*
Bibliographic record
Abstract
The management of type 2 diabetes requires major life style changes. How patients and family members resolve disagreements about disease management affects how well the disease is managed over time. Our goal was to identify differences in how couples resolved disagreements about diabetes management based on ethnicity and patient gender. We recruited 65 Latino and 110 European-American (EA) couples in which one spouse had type 2 diabetes. Couples participated in a 10-minute videotaped, revealed differences interaction task that was evaluated with 7 reliable observer ratings: warm-engagement, hostility, avoidance, amount of conflict resolution, off-task behavior, patient dominance, and dialogue. A series of 2 x 2, Ethnicity x Sex ANOVAs indicated significant effects for Ethnicity and for the Ethnicity x Sex interaction, but not for Sex. Latino couples were rated as significantly more emotionally close, less avoidant, less hostile toward each other, and had less dominant patients than EA couples; however, Latino couples achieved significantly less problem resolution and were more frequently off-task than EA couples. These findings were qualified by patient gender. The findings highlight important differences
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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 | 0.006 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".