Patients’ affective processes within initial experiential dynamic therapy sessions.
Bibliographic record
Abstract
Research has indicated that patients' in-session experience of previously avoided affects may be important for effective psychotherapy. The aim of this study was to investigate patients' in-session levels of affect experiencing in relation to their corresponding levels of insight, motivation, and inhibitory affects in initial Experiential Dynamic Therapy (EDT) sessions. Four hundred sixty-six 10-min video segments from 31 initial sessions were rated using the Achievement of Therapeutic Objectives Scale. A series of multilevel growth models, controlling for between-therapist variability, were estimated to predict patients' adaptive affect experiencing (Activating Affects) across session segments. In line with our expectations, higher within-person levels of Insight and Motivation related to higher levels of Activating Affects per segment. Contrary to expectations, however, lower levels of Inhibition were not associated with higher levels of Activating Affects. Further, using a time-lagged model, we did not find that the levels of Insight, Motivation, or Inhibition during one session segment predicted Activating Affects in the next, possibly indicating that 10-min segments may be suboptimal for testing temporal relationships in affective processes. Our results suggest that, to intensify patients' immediate affect experiencing in initial EDT sessions, therapists should focus on increasing insight into defensive patterns and, in particular, motivation to give them up. Future research should examine the impact of specific inhibitory affects more closely, as well as between-therapist variability in patients' in-session adaptive affect experiencing. (PsycINFO Database Record
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".