Criteria for Decision-Making Capacity: Between Understanding and Evidencing a Choice.
Bibliographic record
Abstract
Although the abilities to understand and to evidence a choice are universally recognised as necessary for decision-making capacity (DMC), they are not sufficient for DMC. Additional criteria such as “appreciation”, “reasoning”, and “using or weighing information” are often used, but the broad and under-defined nature of some of these additional legal criteria has resulted in diverse and sometimes inconsistent interpretations. This article canvasses jurisdictional variations in DMC criteria, focusing on common law and statutory tests in the United States, the United Kingdom and Canada. It proposes a more integrated framework for interpreting DMC beyond the understanding and evidencing a choice criterion by describing how, in addition to the familiar criterion of the ability to form adequate beliefs, “the ability to value” criterion can usefully fill that space. The article illustrates the potential usefulness of this framework by reviewing how the ability to form adequate beliefs and the ability to value are relevant in several challenging cases drawn from the legal literature and clinical experience.
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 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 | 0.212 | 0.387 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.005 | 0.090 |
| Scholarly communication | 0.016 | 0.022 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.013 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".