An Evaluation of the Decision-Making Capacity Assessment Model
Bibliographic record
Abstract
BACKGROUND: The Decision-Making Capacity Assessment (DMCA) Model includes a best-practice process and tools to assess DMCA, and implementation strategies at the organizational and assessor levels to support provision of DMCAs across the care continuum. A Developmental Evaluation of the DMCA Model was conducted. METHODS: = 49) data were collected from practitioners utilizing the Model. RESULTS: Strengths of the Model include its best-practice and implementation approach, applicability to independent practitioners and inter-professional teams, focus on training/mentoring to enhance knowledge/skills, and provision of tools/processes. Post-training, participants agreed that they followed the Model's guiding principles (90%), used problem-solving (92%), understood discipline-specific roles (87%), were confident in their knowledge of DMCAs (75%) and pertinent legislation (72%), accessed consultative services (88%), and received management support (64%). Model implementation is impeded when role clarity, physician engagement, inter-professional buy-in, accountability, dedicated resources, information sharing systems, and remuneration are lacking. Dedicated resources, job descriptions inclusive of DMCAs, ongoing education/mentoring supports, access to consultative services, and appropriate remuneration would support implementation. CONCLUSIONS: The DMCA Model offers practitioners, inter-professional teams, and organizations a best-practice and implementation approach to DMCAs. Addressing barriers and further contextualizing the Model would be warranted.
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 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 | 0.013 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".