Treating anxiety disorders by emotion‐focused psychodynamic psychotherapy (<scp>EFPP</scp>)—<scp>A</scp>n integrative, transdiagnostic approach
Bibliographic record
Abstract
Anxiety disorders are characterized by high levels of anxiety and avoidance of anxiety-inducing situations and of negative emotions such as anger. Emotion-focused therapy (EFT) and psychodynamic psychotherapy (PP) have underscored the therapeutic significance of processing and transforming repressed or disowned conflicted or painful emotions. Although PP provides sophisticated means of processing intrapsychic and interpersonal conflict, EFT has empirically tested a set of techniques to access, deepen, symbolize, and transform emotions consistent with current conceptualizations of emotions and memory. Based on our clinical experience, we propose that an integrative emotion-focused and psychodynamic approach opens new avenues for treating anxiety disorders effectively, and we present a transdiagnostic manual for emotion-focused psychodynamic psychotherapy. The therapeutic approach takes into account both the activation, processing, and modification of emotion and the underlying intrapsychic and interpersonal conflicts. The short-term treatment is based on the three phases of initiating treatment, therapeutic work with anxiety, and termination. Emotional poignancy (or liveliness) is an important marker for emotional processing throughout treatment. Instead of exposure to avoided situations, we endorse enacting the internal process of generating anxiety in the session providing a sense of agency and access to warded-off emotions. Interpretation serves to tie together emotional experience and insight into the patterns and the nature of underlying intrapersonal and interpersonal conflict. Treatment modules are illustrated by brief vignettes from pilot treatments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".