Application of Instance Theory to Real-World Professional Vision: A Randomized Controlled Parallel Design in Clinical Psychology Education
Bibliographic record
Abstract
We aimed to extend instance theory into the domain of real-world professional vision development, examining the effects of explicit rules and exemplars on development of professional vision in a randomized controlled fashion. Participants were novice therapists (N = 48) attending accredited clinical psychology programs in Japan and were randomly divided into four training groups, which received (1) declarative knowledge-based and exemplar-based training, (2) declarative-based training only, (3) exemplar-based training only, and (4) no training. Before, during, and after the training, participants watched an authentic solution-focused brief therapy (SFBT) video and had five minutes to write down their notices regarding the video. Three expert therapists independently evaluated these notices in terms of relevance to SFBT. As hypothesized, novices who received both types of training showed increases in notice relevance during and after the training. In contrast to our hypothesis, novices who received exemplar-based training only showed increased notice relevance during the training. Declarative knowledge with exemplars could provide the best approach for the development of professional vision. Still, many self-motivated learners might learn professional vision in the absence of explicit teaching of declarative knowledge. Future studies should examine the link between participants’ motivation and training effects of mere exposure to exemplars.
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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 | 0.036 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".