Capacity Assessment Training and Competency Evaluation Tool
Bibliographic record
Abstract
Abstract Given the complex interplay of ethical, clinical, and legal factors, evaluating capacities in older adults is an important competency for geropsychologists. However, the amount of quality of training in this area varies, and geropsychology trainees report less confidence in their capacity evaluation skills. To date, only the Pikes Peak Self-Assessment Tool includes items measuring competency and growth in decisional capacity evaluations. However, it is a broad self-report measure assessing general geropsychology competencies. We developed a performance-based measure of decision-making capacity evaluations, the “Capacity Assessment Training and Competency Evaluation Tool (CATCET).” Using the ABA/APA Assessment of Older Adults with Diminished Capacity as a guide, expert panels created two clinical cases across 5 capacity domains. This presentation will discuss the creation of the CATCET, its application as a training and evaluation tool, and initial performance data among psychology graduate students, intern, and fellows across settings.
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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 | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".