Evaluating Adult’s Competency: Application of the Competency Assessment Process
Bibliographic record
Abstract
Competency assessment of adults with cognitive impairment or mental illness is a complex process that can have significant consequences for their rights. Some models put forth in the scientific literature have been proposed to guide health and social service professionals with this assessment process, but none of these appear to be complete. A new model, the Competency Assessment Process (CAP), was presented and validated in other studies. This paper adds to this corpus by presenting both the CAP model and the results of a survey given to health and social service professionals on its practical application in their clinical practice. The survey was administered to 35 participants trained in assessing competency following the CAP model. The results show that 40% of participants use the CAP to guide their assessment and the majority of those who do not yet use it plan to do so in the future. A large majority of participants consider this to be a relevant model and believe that all interdisciplinary teams should use it. These results support the relevance of the CAP model. Further research is planned to continue the study of the application of CAP in healthcare facilities.
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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 | 0.037 | 0.088 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".