A Prototype-Based Model of Adult Attachment for Clinicians
Bibliographic record
Abstract
Models of adult attachment have proven to be useful for understanding illness behavior, stress responses, susceptibility to disease processes, and psychotherapeutic approaches to difficult patients. Two methods of assessing patterns of attachment, using self-report instruments and using techniques such as the Adult Attachment Interview (AAI), are only weakly related and each has drawbacks for clinical use. We have previously assessed commonalities and differences in the descriptions of attachment patterns that emerge from these schools and synthesized them in empirically based attachment prototypes. In this companion article, we describe a prototype-based model of attachment. This model defines dimensions of attachment anxiety and attachment avoidance as composites of particular aspects of internal working models of self and other, behavior in current close relationships, patterns of expression of affect, and narrative coherence. The model emphasizes the clinical importance of the severity of attachment insecurity, defined as a dimension which incorporates problems in the previously listed domains of attachment as well as deficits in mentalizing, self-agency, and resolution of trauma. The model locates the central tendencies of prototypic ("textbook") descriptions of four patterns of attachment (secure, dismissing, preoccupied, and fearful/disorganized) while avoiding definitions of the boundaries between categories of attachment. We compare the prototype-based model to the two most prominent current models of attachment, the 4-category, 2-dimension model derived from self-report methods of assessment and categories of "attachment states of mind" derived from the Adult Attachment Interview. Finally, we discuss limitations of the prototype-based model and areas requiring further research.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".