Developing expertise in psychotherapy: The case for process coding as clinical training.
Bibliographic record
Abstract
Routine outcome monitoring (ROM) is a major development in the field since it offers likely outcome trajectories and is particularly helpful for failing cases. However, ROM has not led to improved skill development more generally, and it is debatable as to whether expertise is even possible to acquire in psychotherapy. What is missing but crucial to expertise is feedback on the outcome of one's actions in real time, which would enable responsive adjustments and improve outcomes. It is argued in this article that by identifying empirically validated moment-to-moment markers capable of differentiating later clinical outcomes, process researchers have uncovered the possibility of extracting prognostic information in real time, but one must develop the requisite observational skills. Multiple lines of research are reviewed to support the contention that real-time outcome information is available to guide responsivity and improve outcomes. And the typically hidden nature of these important signals further underscores the need for systematic training in process acuity. Given the pressing need to improve training methods, process coding training should not be restricted to research laboratories but should be exported to the clinical setting and tailored to the needs of clinicians for use in real time during therapy sessions. These are testable hypotheses that, if successful, hold the possibility of improving training and reversing the worrying trend of experience in psychotherapy being unrelated to outcome. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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 | 0.021 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".