Termination in 16-session accelerated experiential dynamic psychotherapy (AEDP): Together in how we say goodbye.
Bibliographic record
Abstract
This article explores key aspects of the termination process in a 16-session treatment protocol of accelerated experiential dynamic psychotherapy (AEDP). AEDP theory and its empirical support are described; interventions used throughout termination are demonstrated with verbatim clinical exchanges; and potential challenges faced during termination are addressed. Congruent with AEDP's healing orientation, termination is reframed as completion and launching: Although treatment ends, the change process begun in therapy can continue, as does the therapist's care for the patient. AEDP interventions during termination include (a) relational strategies to foster connection and undo aloneness; (b) the highlighting of patient resilience and the celebration of growth; (c) affirmative work with defenses around loss; (d) coregulation of patient's emotional experience; (e) experiential, bodily-rooted affective strategies to process and transform negative emotions; and (f) thorough exploration and processing of ensuing, vitalizing positive emotions and in-session experiences of change-for-the-better (i.e., metatherapeutic processing), to expand these and promote enhanced well-being and flourishing. Therapists aim to (a) elicit and process emotions related to the completion of treatment; (b) celebrate patients' affective achievements; and (c) convey trust and confidence in an ongoing transformational process, predicted to yield not only diminishment of symptoms and suffering but also upward spirals of flourishing. AEDP suggests that in providing patients a new, positive attachment experience of togetherness as therapy ends, termination offers a unique opportunity to disconfirm patients' earlier attachment-based expectations, revise inner working models, and help patients grow in self-confidence as they face, accept, and thrive in the wake of loss. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".