Does Personality Matter in Diabetes Adherence? Exploring the Pathways between Neuroticism and Patient Adherence in Couples with Type 2 Diabetes
Bibliographic record
Abstract
BACKGROUND: Personality has received some attention in the Type 2 diabetes literature; however, research has not linked personality and diabetes adherence behaviors (diet and exercise), identified pathways through which they are associated, nor taken into consideration important contextual factors that influence behavior (the patient's partner). METHODS: Dyadic data from 117 married, heterosexual couples in which one member is diagnosed with Type 2 diabetes was used to explore associations between each partner's neuroticism and patient dietary and exercise adherence through the pathways of negative affect, depression symptoms, and couple-level diabetes efficacy (both patient and spouse report of confidence in the patient's ability to adhere to diabetes management regimens). RESULTS: Results revealed that higher levels of neuroticism were associated with lower patient dietary and exercise adherence through (1) higher levels of depression symptoms (for patients' neuroticism) and negative affect (for spouses' neuroticism), and (2) lower levels of couple-level diabetes efficacy. CONCLUSIONS: The results from this study provide evidence that both patient and spouse personality traits are associated with patient dietary and exercise adherence through increased emotional distress-albeit different emotional pathways for patients and spouses-and lower couple confidence in the patients' ability to manage their diabetes.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".