Attachment and Psychosomatic Medicine: Developmental Contributions to Stress and Disease
Bibliographic record
Abstract
OBJECTIVE: The object of this study was to evaluate the evidence linking attachment insecurity to illness. Attachment theory describes lifelong patterns of response to threat that are learned in the interaction between an infant and his or her primary caregiver. Despite its biopsychosocial domain, attachment theory has only recently been applied to psychosomatic medicine. METHOD: MEDLINE and PsychInfo databases were searched from 1966 to 2000 for English language papers with key words "attachment" and "object relations." Papers and their cited references were reviewed if they were directly related to physical illness, symptoms, or physiology. A hypothetical causal model was developed. RESULTS: Direct and indirect evidence from survey studies supports an association between attachment insecurity and disease. Animal studies and human experiments suggest that attachment contributes to individual differences in physiological stress response. There is also less robust support for insecure attachment leading to symptom reporting and to more frequent health risk behaviors, especially substance use and treatment nonadherence. Evidence supports the prediction from attachment theory that the benefits of social support derive more from attachment relationships than nonattachment relationships. CONCLUSIONS: Although the available data are suggestive rather than conclusive, the data can be organized into a model that describe attachment insecurity leading to disease risk through three mechanisms. These are increased susceptibility to stress, increased use of external regulators of affect, and altered help-seeking behavior. This model warrants further prospective investigation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".