A Case for the Standardized Assessment of Testamentary Capacity
Bibliographic record
Abstract
BACKGROUND: With an increasingly aged, frail population that holds a disproportionate amount of wealth, clinicians (especially those with expertise in older adults) may be asked with more frequency to offer a clinical opinion on testamentary capacity (TC), the mental capacity to make a will. METHOD: This paper reviews the legal criteria as well as the empirical research on assessment tools for determining testamentary capacity (TC). We also review the relevance of instruments used for the assessment of other decisional capacities in order to evince the potential value of developing a standardized assessment of TC for clinician experts. RESULTS: The legal criteria, often referred to as a "test", for determining requisite TC (Banks v. Goodfellow) have remained much the same since 1870 with minimal clinical input and, as such, there has been little development in TC assessment instruments. Decisional instruments designed to assess Consent to Treatment may have relevance for TC. CONCLUSION: We make the case for a semi-structured interview that includes standardized criteria for the legal test for TC, supplemented by a validated brief neuropsychological assessment, which together comprise a Contemporaneous Assessment Instrument (CAI) for TC.
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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 | 0.063 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.001 | 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".