407 - The Baycrest Quick-Response Caregiver Tool: The Role for a New Tool for Caregivers of Persons with Dementia
Bibliographic record
Abstract
Background: Responsive behaviours in dementia are associated with poor outcomes for the person with dementia (PWD) and caregiver burnout. Family caregivers need a variety of tools to manage responsive behaviours. The Baycrest Quick-Response Caregiver Tool was developed to provide caregivers with a tool that can be used in real time. In this study, the feasibility, impact, and effectiveness of this new tool were studied in family caregivers and health care providers (HCP) using quantitative and qualitative measures. Methods: Family caregivers were recruited and were asked to complete a pre-survey before being sent the link to the educational tool. One month after the telephone survey, caregivers were sent an online post-survey to gather their feedback on the tool and the impact of the tool on caregiver well -being. Healthcare providers were also recruited and reviewed the tool through an online feedback survey. The feasibility, impact, and effectiveness of the tool were assessed using quantitative and qualitative measures. Results: Caregivers had a moderate degree of and reported a high level of competence - these scores were maintained throughout the study. Caregivers reported that tool positively impacted their compassion towards the person with dementia (PWD), and that their interactions with improved. 100% of HCP who completed the feedback survey would recommend the tool to other HCP and to caregivers of PWD. The caregivers and HCP provided specific suggestions for improvement. Conclusions: The Baycrest Quick-Response Caregiver Tool was found to be feasible and helpful. It provides caregivers and HCP with an additional approach for responsive behaviours.
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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 | 0.014 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.007 |
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".