Attachment Trauma Re-Viewed: A Commentary on Farina and Schimmenti (2025a).
Bibliographic record
Abstract
Farina and Schimmenti (2025a) have made a major contribution to furthering our understanding of early developmental trauma and its serious, long-term, psychosocial effects, while pointing toward therapeutic approaches deeply informed by their rich conceptualization of attachment trauma (AT) and clinical acumen. The authors' impressive review and integration of a wealth of developmental and clinical research has led them to propose a sophisticated yet clear definition of AT, as well as important notions on its relationship with disorganized attachment (DA) and a helicopter view on its treatment. We identify several appreciations of the authors' conceptualization and applications to clinical practice; offer suggestions for further clarification and expansion of ideas; and finally propose how we might "continue the conversation" with an emphasis on clinical theory and practice, past, present, and future. Farina and Schimmenti are to be strongly commended for providing a solid foundation from which to pursue, with greater clarity and intention, our field's ever-deepening and hope-engendering understanding and treatment of survivors of AT, for patients, therapists, and concerned loved ones.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.056 | 0.059 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".