The Development and Implementation of a Decision-Making Capacity Assessment Model
Bibliographic record
Abstract
BACKGROUND: Decision-making capacity assessment (DMCA) is an issue of increasing importance for older adults. Current challenges need to be explored, and potential processes and strategies considered in order to address issues of DMCA in a more coordinated manner. METHODS: An iterative process was used to address issues related to DMCA. This began with recognition of challenges associated with capacity assessments (CAs) by staff at Covenant Health (CH). Review of the literature, as well as discussions with and a survey of staff at three CH sites, resulted in determination of issues related to DMCA. Development of a DMCA Model and demonstration of its feasibility followed. RESULTS: A process was proposed with front-end screening/problem- solving, a well-defined standard assessment, and definition of team member roles. A Capacity Assessment Care Map was formulated based on the process. Documentation was developed consisting of a Capacity Assessment Process Worksheet, Capacity Interview Worksheet, and a brochure. Interactive workshops were delivered to familiarize staff with the DMCA Model. A successful demonstration project led to implementation across all sites in the Capital Health region, and eventual provincial endorsement. CONCLUSIONS: Concerns identified in the survey and in the literature regarding CA were addressed through the holistic interdisciplinary approach offered by the DMCA Model.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".