Australian lawyers' views on competency issues in older adults
Bibliographic record
Abstract
Lawyers are often called upon by their older clients to draft contracts, make up wills or powers of attorney, or provide other forms of legal advice or service. With the increasing numbers of older people in the population, the numbers of older clients for legal practitioners is likely to increase. Older people are also at increased risk for impaired cognitive processes that can affect their ability to make reasoned judgements in civil contracts and other legal arrangements. Here we report on the results of a survey of 302 Australian solicitors as to their experience and practices in determining the capacity of older clients to make legal decisions. The responses, reflecting a 20.7% return rate, showed a wide range of experience in conducting capacity assessments, with a mean of 15.7 years (SD = 10.59). There was a very wide range of practices in determining capacity with no consensus. The most frequent form of questions older clients were asked related to personal and family history. Less than one-quarter asked for the rationale of the decision: the most appropriate form of question in the literature. The results suggest a need for further training of solicitors in the assessment of the capacity of older clients to make legal decisions.
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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 | 0.012 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".