Power of Attorney in Ontario: A Study of the Information Behaviours of Attorneys in the Contexts of the Substitute Decisions Act, 1992, Capacity, and an Aging Population
Bibliographic record
Abstract
This dissertation documents a study of the information behaviours of substitute decision-makers (attorneys) exercising Power of Attorney (POA) in Ontario for older relatives with compromised decisional capacity (grantors) as they deteriorated. This research identifies how attorneys acquired the information they needed to execute their fiduciary responsibilities under Ontario’s Substitute Decisions Act, 1992, which gives attorneys the authority to make property and personal care decisions on behalf of another person. Context was an integral part of this study: the development of “continuing power of attorney” law in Ontario, plus the Fram Report with its model of care based on preserving the person’s autonomy, combined with the aging of the Baby Boom generation, and changing social attitudes to the loss of capacity and other infirmities that accompany aging, form the background to this research. The central finding of the study was that attorneys adapted their information acquisition strategies and actions to the changes in the physical and/or mental health of their grantors as they declined toward death. The study was based on interviews with eight attorneys from the Greater Toronto Area (plus two from a pilot study), all of whom had responsibility for the care of one-generation-older relatives. Interviews, which were transcribed and process coded, revealed nine themes. Endsley’s (1995) theory of Situation Awareness was used to examine the period of dormant POA authority between the time grantors assigned POA authority and the attorneys’ decisions to activate that authority on reasonable grounds. Ellis’s (1989) and Ellis et al.’s (1993) information search models were used to investigate attorney information acquisition activities from the moment the attorneys began to perform their POA duties throughout their tenure. Attorneys described how they searched for and found their grantors’ financial information, moved them to retirement and long-term care homes, hired caregivers to enable them to age in place, and made numerous other decisions for their well-being. The findings of this study confirmed many of the findings of the Law Commission of Ontario in its 2017 Report, Legal Capacity, Decision-making and Guardianship, especially its finding of how little attorneys knew about the statutes that governed their decision-making activities.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.022 | 0.011 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".