Évaluation de l’aptitude d’une personne âgée atteinte de déficits cognitifs à gérer sa personne et ses biens : Identification des outils disponibles
Bibliographic record
Abstract
The accelerated aging of the Canadian population is a recognized fact and leads to an increasing number of seniors with cognitive impairments (Curateur public du Québec, 2010a). This has a definite impact on health professionals who have to assess their competency to live independently and manage their finances. This decision, which has important consequences for the person, must be based on an objective and rigorous assessment. The purpose of this paper is to analyse the available tools, both in the scientific literature and in clinical settings, to better document the various components to assess seniors' competency to live independently and manage their finances. The goal is to help practitioners who work with older people with cognitive impairments to accurately assess their ability to manage themselves and their property. A review of the relevant literature and training available, as well as three group consultations, showed that there is no consensus about the tools used to assess the capacity to take care of oneself and one's property. Additional studies are thus needed to fill the gap in knowledge about specific tools used to assess competency.
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 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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".