The Measurement of Interview Structure in Five Types of Psychiatric and Psychotherapeutic Interviews
Bibliographic record
Abstract
In a companion report (Beck & Perry, 2008), we reviewed the literature with regard to interview structure from which we derived seven operationalized quantitative measures. This report examines these measures as applied to five commonly used interview types--psychodynamic therapy sessions, dynamic interviews, Relationship Anecdote Paradigm (RAP) interviews, the Guided Clinical Interview and the Structured Clinical Interview for the DSM-IV axis I--each administered to the same six patients (n = 30). Two clinicians independently rated each interview using the Global Level of Interview Structure Scale (GLISS). Both the GLISS and six of the seven operationalized measures differed across interview types but not between subjects. Factor analysis yielded a single factor solution composed of five measures, not including a sixth measure (percentage of interviewer interventions that were questions) which was used as a solitary variable. Together the single factor and the percentage of questions predicted 75.2% of the variance in GLISS ratings, although no association was found between the factor and the percentage of questions. The GLISS and the operationalized measures captured distinct but complementary dimensions of interview structure. Discriminant analysis indicated that, on average, 80% of all interviews were correctly classified as to their type. Our main findings confirm that we can now accurately measure the degree of interview structure. Further research is needed to examine how these measures apply to other interview settings, such as psychoanalytic or cognitive-behavioral treatments, in the social sciences.
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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 | 0.062 | 0.175 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".