Attachment avoidance and health-related quality of life: Mediating effects of avoidant coping and health self-efficacy in a rehabilitation sample.
Bibliographic record
Abstract
PURPOSE/OBJECTIVE: The onset of chronic illness or disability (CID) can be conceptualized as a threat that activates the attachment system. Moreover, the waxing-and-waning nature of CID-related symptoms and management of acute and chronic illness stressors means that the attachment system may be repeatedly activated. Contending with repeated threats to health (i.e., security) can complicate psychosocial adjustment to CID and can negatively impact health-related quality of life (HRQoL). Adjustment to CID requires intrapersonal resources, such as adaptive coping and self-efficacy. In spite of attachment theory's relevance to conceptualizing adaptation to CID, no models of psychosocial adaptation to CID account for individual differences in coping behaviors and health self-efficacy through an attachment lens. This limits future theory-driven research. Thus, the present study proposes and tests an integrated model of psychosocial adaptation to CID using an attachment framework. Research Method/Design: Participants in this study included adults referred for psychological services at a tertiary care physical rehabilitation center between 2016 and 2020. Ninety adults completed measures of attachment anxiety and attachment avoidance, coping, health self-efficacy, and HRQoL at one time point. RESULTS: Path analysis indicated that the proposed model fits the data well. Higher attachment avoidance was significantly related to lower HRQoL, as mediated by higher avoidant coping and lower health self-efficacy. CONCLUSIONS/IMPLICATIONS: Results suggest that individuals high on attachment avoidance may require additional support to move toward psychosocial adaptation. Further research examining the role of attachment insecurity dimensions in adaptation to CID is warranted and should include longitudinal designs to replicate these findings. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".