The problematic impressions of therapy scale: a transtheoretical measure of negative patient experience in psychotherapy
Bibliographic record
Abstract
OBJECTIVE: Although negative experiences in psychotherapy are commonly reported by patients, these phenomena are understudied. The present study introduces the Problematic Impressions of Therapy Scale (PITS), a new measure designed to assess distressing in-session experiences. METHODS: = 545), we conducted exploratory factor analysis followed by exploratory structural equation modeling to determine the underlying factor structure and psychometric properties of the PITS. RESULTS: Results supported a 21-item, two-factor model reflecting therapist "errors of commission" (e.g., pressuring disclosure, expressing judgment) and "errors of omission" (e.g., failing to provide support or direction). Both subscales demonstrated acceptable internal consistency and good model fit across U.K.- and U.S.-based samples. In tests of concurrent validity, higher PITS scores showed moderate-to-strong negative associations with measures of the working alliance (Working Alliance Inventory) and perceived therapist responsiveness (Patient's Experience of Attunement and Responsiveness), suggesting that more frequent or distressing negative experiences correlate with weaker alliances. Further, errors of omission uniquely predicted poorer ratings of both bond and task dimensions of the alliance. CONCLUSION: These findings support the reliability and construct validity of the PITS as a tool for identifying and quantifying negative or hindering experiences in psychotherapy.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".