Impact of Adult Attachment Styles on Pain and Disability Associated With Arthritis in a Nationally Representative Sample
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to evaluate Mikail et al.'s hypothesis that adult attachment styles are associated with important pain-related variables such as pain and disability levels. DESIGN: A cross-sectional design was used to examine the relation between measures of adult attachment styles and both pain and disability. SETTING: The data used were obtained from the National Comorbidity Survey, a large and nationally representative sample of community-dwelling individuals aged 15 to 54 years. In the present study, individuals (n = 381) in the National Comorbidity Survey with arthritis or related conditions were included. OUTCOME MEASURES: Ratings regarding three adult attachment styles (secure, anxious, and avoidant) were obtained by administering Hazan and Shaver's attachment self-report in an interview format. Pain and disability were assessed in a similar manner using four-point rating scales. RESULTS: Ratings of insecure attachment were positively and significantly correlated with both pain and disability. A multiple regression analysis revealed that pain severity and the rating of anxious attachment could account for 20.3% of the variance in disability. CONCLUSIONS: The attachment theory holds promise for understanding reactions to pain conditions, and Mikail et al.'s model warrants further 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 | 0.002 | 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 | 0.000 | 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".