Relationship between insecure attachment and physical symptom severity is mediated by sensory sensitivity
Bibliographic record
Abstract
Abstract Objective Various models have been used to explain somatization, including attachment theory, which describes how formative experiences influence perceptions of vulnerability and threat. Although attachment insecurity is associated with greater physical symptoms, the mechanisms by which attachment insecurity influences the experience of physical symptoms are not clear. Sensory processing sensitivity (SPS) describes a low threshold to responding to stimuli and high emotional reactivity. It is associated with both attachment insecurity and physical symptoms. The purpose of this study is to test a model in which attachment insecurity, depression, and SPS interact to influence physical symptoms. Methods Cross‐sectional data from the online Self‐Assessment Kiosk were used ( N = 186). Participants were surveyed regarding attachment insecurity (ECR‐M16), physical symptom severity (PHQ‐15), sensory processing sensitivity (HSPS), and depression (PHQ‐9). A path analysis was used to analyze the data. Results Modal participants were white (74%) single (45%) women (80%) with university education (79%). Attachment anxiety, attachment avoidance, and sensitivity were correlated with physical symptom severity. The data suggested that sensitivity mediates between attachment anxiety and physical symptoms ( β indirect = 0.070, p = .003 and β direct = −0.030, p > .05) and this relationship remains significant when controlling for depression. Conclusions This study extends our understanding of the potential pathways that lead individuals with attachment insecurity to experience burdensome physical symptoms by supporting a mediating role for SPS.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.007 | 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".