Assessing Capacity Within a Context of Abuse or Neglect
Bibliographic record
Abstract
In 2000, with the implementation of Part III of the Adult Guardianship Act: Support and Assistance for Abused and Neglected Adults, British Columbia formally recognized the need to examine issues of decisional capacity of older adults within a context of abuse or neglect. Interestingly, however, although the test of capacity was clearly laid out under this piece of legislation, the potential influence that living in a situation of abuse or neglect may have on how the person makes decisions is not explicitly addressed. Similarly, this is a missing link throughout the literature discussing decisional capacity in older adults. This gap exists despite the fact that determining the "protection" needs of someone who is being abused and/or neglected often hinges directly on that person's decisional capacity. The purpose of this article is to examine the unique aspects associated with assessing and determining capacity for older adults who are living in a situation of abuse or neglect. The specific objectives are to: (a) examine how living in a situation of abuse or neglect may influence the determination of capacity and (b) explore the implications of conducting an assessment within a potentially abusive context. The legal notion of undue influence and the psychological concept of relational connection are introduced as potentially important for considering decision making within this context.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".