Therapist verification of patient self-concepts as a responsive precondition for early alliance development and subsequent introject change
Bibliographic record
Abstract
OBJECTIVE: , any interpersonal exchange may become frustrated, anxiety-riddled, and at risk for deterioration. Thus, it may be important for therapists to meet patients' self-verification needs as a responsive precondition for early alliance establishment and development. We tested this hypothesis with patients receiving cognitive behavioral therapy for generalized anxiety disorder-a condition that may render one's self-verification needs especially strong. We also tested the hypothesis that better early alliance quality would relate to subsequent adaptive changes in and posttreatment level of patients' self-concepts. METHOD: Eighty-four patients rated their self-concepts at baseline and across treatment and follow-up, their postsession recollection of their therapist's interpersonal behavior toward them during session 2, and their experience of alliance quality rated after sessions 3-6. RESULTS: As predicted, the more therapists verified at session 2 a patient's baseline self-concepts (which trended toward disaffiliative and overcontrolling, on average), the more positively that patient perceived their next-session alliance. Moreover, better session 3 alliance related to more adaptive affiliative and autonomy-granting self-concepts at posttreatment. CONCLUSION: Results are discussed within a therapist responsiveness framework.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".