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Record W1605124053 · doi:10.1002/bsl.592

Australian lawyers' views on competency issues in older adults

2004· article· en· W1605124053 on OpenAlexaboutno aff
Edward Helmes, V A Lewis, Alfred Allan

Bibliographic record

VenueBehavioral Sciences & the Law · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Education and Practice Innovations
Canadian institutionsnot available
FundersEdith Cowan University
KeywordsQuarter (Canadian coin)Affect (linguistics)PopulationLegal advicePsychologyLegal serviceOlder peoplePublic relationsSocial psychologyLawActuarial scienceBusinessMedicineGerontologyPolitical science

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.936
Threshold uncertainty score0.956

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.121
GPT teacher head0.462
Teacher spread0.341 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2004
Admission routes1
Has abstractyes

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