DEVELOPMENT OF A PERSON-CENTRED COGNITIVE TOOL: HOW TO PROVIDE STRENGTH-BASED MEMORY CARE
Bibliographic record
Abstract
As the aging population grows, there is an increasing focus on interventions that support person-centred and wellness-based approaches to care for older adults. This is particularly evident for those in supported living (SL) centres and the community, where the focus is on maximizing strengths, and reducing the need for admission to long-term or acute settings for more advanced care. Despite this, current cognitive assessment tools focus primarily on diagnosis or functional deficits in order to determine care needs, while there are limited strength-based and person-centered cognitive tools available. Our research challenges this practice gap through understanding the ‘gold standard’ of person-centered care, in order to develop a tool that supports a wellness-focused approach to meet residents’ everyday lifestyle goals and health support needs. This participatory action research study is a partnership between the Geriatric Research Unit at the University of Calgary and United Active Living, a SL facility in Calgary. In this study, researchers work alongside Memory Care staff and cognitive residents in developing an assessment approach that incorporates resident goals, memory care programming considerations, and cognitive support to maximize resident well-being. This presentation will argue for critical engagement with a person-centred care philosophy that moves from the rhetoric of the approach to the application of the philosophy in relation to assessment. We will overview the process involved in developing the tool and present the draft assessment tool for discussion.
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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 | 0.041 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".