Bibliographic record
Abstract
Abstract The main objective of this chapter is to provide an overview of clinical interviewing. Although clinical interviewing is often referred to as an art (Shea, 2007), the information in this chapter highlights the science of clinical interviewing as well. The chapter opens with a discussion of the general structure and content of clinical interviews that are typically conducted in mental health contexts. The reader is introduced to a variety of interviews that are used in the assessment of Axis I and Axis II conditions, including their psychometric properties; guidelines for the assessment of suicidality are also presented. This is followed by an overview of interviewing skills. Specifically discussed are ways in which information processing limitations, verbal and nonverbal cues, and style of questions can influence the clinical interview. We then turn to a discussion of case formulation, a core component of the clinical interview. Empirical research on the impact of training on quality of case conceptualization and on the association between case formulation and treatment outcome is summarized. The chapter closes with a brief overview of issues that may arise when interviewing certain populations, in particular, couples, individuals from diverse populations, and young individuals.
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 | 0.036 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".