Facilitating implementation of the Decision-Making Capacity Assessment (DMCA) Model: senior leadership perspectives on the use of the National Implementation Research Network (NIRN) Model and frameworks
Bibliographic record
Abstract
OBJECTIVE: Dementia and other chronic conditions can compromise a person's ability to make independent personal and financial decisions. In the wake of an ageing population and rising incidence of chronic conditions, the number of persons who may require Decision-Making Capacity Assessments (DMCAs) is likely to increase. Legislation (e.g., Trusteeship, Guardianship, Medical Assistance in Dying) also necessitates that DMCAs adhere to legislative requirements and principles. An intentional, explicit and systematic means of implementing standardized DMCA best-practices is advisable. This single exploratory case-study examined the perspectives of senior leaders and clinical experts regarding the utility of using the National Implementation Research Network (NIRN) Model to facilitate implementation, spread and sustainability of a DMCA Model. Participants learned about the NIRN Model and discussed its application during working and focus groups, all of which were audio-recorded, transcribed, and analyzed using thematic analysis. RESULTS: Participants found that the NIRN Model aligned well with the DMCA Model, and offered utility to support implementation, spread and sustainability of DMCA best-practices. Participants also noted barriers related to its language, inability to capture personal change, resource requirements, and complexity. It was recommended that a NIRN-informed DMCA-specific implementation framework and toolkit be developed and NIRN-champions be available to guide implementation.
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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 | 0.031 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".