Associations between adult attachment ratings and health conditions: Evidence from the National Comorbidity Survey Replication.
Bibliographic record
Abstract
OBJECTIVE: Attachment insecurity has been hypothesized to be a risk factor for the development of disease and chronic illness. This study was the first to investigate associations between adult attachment ratings and a wide range of health conditions. DESIGN: Cross-sectional data from the National Comorbidity Survey Replication (N = 5645) were used. MEASURES: Participants completed Hazan and Shaver's (1987) measure of adult attachment and provided reports regarding 15 health conditions. RESULTS: Logistic regression analyses that adjusted for demographic variables indicated that avoidant attachment ratings were positively associated with conditions defined primarily by pain (e.g., frequent or severe headaches). Anxious attachment ratings were positively associated with a wider range of health conditions, including several involving the cardiovascular system (i.e., stroke, heart attack, high blood pressure). Secure attachment ratings were unrelated to the health conditions. Additional analyses investigated whether the attachment ratings accounted for unique variance in the health conditions beyond that accounted for by lifetime histories of depressive, anxiety, and alcohol- or substance-related disorders. In these analyses, anxious attachment ratings continued to have significant positive associations with chronic pain, stroke, heart attack, high blood pressure, and ulcers. CONCLUSION: The findings were generally supportive of the theory that insecure attachment is a risk factor for the development of disease and chronic illness, particularly conditions involving the cardiovascular system. Further research regarding the role of attachment in the development of specific health conditions is warranted.
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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 | 0.011 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".