Bibliographic record
Abstract
We examined psychodynamic interview tasks and techniques to identify clinical actions that improve or impede exploration of subjects' emotional responses, conflicts, defenses, and central relationship themes. This article extends previous quantitative studies (Perry, Fowler, & Greif, unpublished; Perry, Fowler, & Semeniuk, 2005) by examining interview vignettes in 50-minute psychodynamic research interviews. We conducted qualitative analyses on 72 dynamic research interviews given by 26 subjects to delineate categories of tasks and interventions. Results indicated five broad tasks of the dynamic interview: 1) Frame Setting; 2) Offering Support; 3) Exploring Affect; 4) Offering Trial Interpretations; and 5) Providing a Formulation and Feedback of relationship themes and conflicts. We further selected two interviews each from 10 subjects, in which there was a difference of one standard deviation or greater on the Overall Dynamic Interview Adequacy scale (Perry, 1999), and interviewer errors from the Therapeutic Alliance Analogue scale (Perry, Brysk, & Cooper, 1989). We utilized excerpts from these interviews to highlight the importance of these tasks and techniques in deepening discussion of dynamically meaningful material.
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 | 0.035 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".