Subject and Interviewer Determinants of the Adequacy of the Dynamic Interview
Bibliographic record
Abstract
The dynamic interview is widely used in clinical and research settings, but factors associated with an adequate dynamic interview have not been systematically studied. Twenty-six subjects had a median of 3 dynamic interviews at least 6 months apart, conducted by 13 clinician interviewers. We examined the resulting 72 videotaped interviews for predictors of dynamic interview adequacy. Overall Dynamic Interview Adequacy was not explained by subject, occasion, or interviewer, per se, although significant negative effects were found for personality disorder and borderline personality scores and diagnoses of major depression and dysthymia, and positive effects for interviewer experience. Dynamic adequacy was related to overall breadth and depth of topic coverage (R2 = 0.424). However, in stepwise multiple linear regression, 5 therapeutic alliance factors together contributed an R2 = 0.768, diminishing the effect of topic coverage. Adequate dynamic interviews established a positive subject-interviewer interaction, facilitated subject exploration, and minimized technical interviewing errors, thereby yielding sufficient topic coverage.
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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 | 0.055 | 0.197 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".