Attachment and group psychotherapy: Introduction to a special section.
Bibliographic record
Abstract
The application of attachment theory to adult psychotherapy represents a growing area of research and practice. Despite the conceptual overlap between group therapeutic factors, attachment theory, and therapeutic tasks as outlined by Bowlby (1988), there is little research on attachment functioning in group therapy. Hence, there remain substantial questions about the role of attachment theory in understanding group therapy processes and outcomes. The three studies in this special section advance the research in some of these important areas, including showing that positive changes in self-reported attachment insecurity among clients persist long after group therapy ends; attachment anxiety affects the level and rate of interpersonal learning in groups; and change in attachment to the therapy group has an impact on longer term change in individual group members' attachment. Each article also examines the impact of these attachment concepts on treatment outcomes. Numerous areas remain to be explored when it comes to the implications of attachment theory for understanding and conducting group therapy, including the conceptual and practical overlap between attachment concepts such as security and exploration with group therapeutic factors such as cohesion and interpersonal learning. The articles in this special section begin to address some of these issues related to attachment theory and its implications for group therapists.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".