The professionalization and training of psychologists: The place of clinical wisdom
Bibliographic record
Abstract
OBJECTIVE: The current study examines how clinical wisdom develops and how it both is and can be influenced by professional training processes. In this way, the project is studying the intersection of developmental and systemic processes related to clinical wisdom. METHOD: Researchers analyzed the interviews of psychologists practicing in the USA and Canada who were nominated for their clinical wisdom by their peers. These interviews explored how graduate training and professionalization were thought to influence the development of clinical wisdom and were subjected to an adapted grounded theory analysis. RESULTS: The findings described both professional and personal disincentives toward developing wisdom, including the dangers of isolation. Therapists reported concerns about educational systems that rewarded quick answers instead of thoughtful questioning in processes of admittance, training, and accreditation. Findings emphasized the importance of teaching multiple psychotherapy orientations, critical self- and professional-reflection skills, and openly supporting graduate students' curiosities and continued professional engagement. CONCLUSIONS: Recommended principles for training are put forward for the development and evaluation of psychotherapy training programs that aim to foster clinical wisdom. These principles complement training models focused upon clinical competence by helping trainees to develop a foundation for clinical wisdom.
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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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.028 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| 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".