Bibliographic record
Abstract
Expertise in Counseling and Psychotherapy features seven master therapist studies from around the world and provides an extensive synthesis of these studies to produce the first international perspective of expert counselors and psychotherapists. The study of expertise has a rich history, whereas research on psychotherapy expertise has mostly surfaced in the past two decades. Jennings and Skovholt first applied qualitative methodology to the study of expert therapists in 1996. Qualitative research has proven to be an extremely effective method for capturing the complexity of the master therapist construct. One limitation of this line of research is that most studies have been conducted in the United States. Fortunately, there are a small but growing number of international qualitative studies on psychotherapy expertise. Moreover, these studies utilized essentially the same research questions and methodologies as our first study on expert therapists, making the consolidation of the findings seamless and trustworthy. The studies include three therapist expertise research projects in Southeast Asia, including Singapore, Japan, and Korea. In North America, there are studies from the United States and Canada. In Europe, there are studies from Portugal and the Czech Republic. The qualitative meta-analysis of all seven data sets is the highlight of our book on master therapists from around the world. The findings and recommendations from this book will enhance the training of future psychotherapists and counselors. Understanding the universal characteristics of expert therapists practicing around the world offers training programs and mental health practitioners a heuristic for optimal therapist and counselor development.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".