Pushing the Boundaries of Integrated Care at the Seniors Health and Wellness Village at Peel Manor in Ontario Canada
Bibliographic record
Abstract
Seniors living with dementia spend more time in emergency departments, have higher hospitalization rates than other Canadians aged 65 and older, and are at higher risk of being inadequately served due to the complexity of their health and socio-economic needs, lack of timely access to resources and specialized care. Service providers are siloed in their communication thereby increasing risk for self-navigation of the system and having to retell their story to health providers, in an unintegrated health system. Within Ontario, Canada there is strong focus on long-term care and alternative level of care hospital beds for seniors; a costly way to care compared to more cost-effective solutions accessible through home-based community care. Ontarian seniors prefer to age at home in their community. Health equity is complex and multifaceted thus requiring new ways of working to ensure no one is left behind. Peel Region has built a Seniors Health and Wellness Village (SHWV) to address gaps and inequities for seniors living with dementia and their caregivers. The SHWV includes Adult Day Services, Short Stay Overnight Respite, Long Term Care, and an Integrated Care Centre delivering seniors-focused primary care. Services will fundamentally be restructured to enable timely, coordinated wrap-around, person-centred community-based care which includes weekly Interdisciplinary Team Care Rounds, proactively prioritizing the care and well-being of seniors and their caregiver, better known as the caregiving-dyad. An iterative approach was taken to monitor, measure and inform customized decision-making that better informs interventions thereby enhancing quality of life and improved service experiences for clients, caregivers and staff. Leveraging partnerships and streamlining communications to improve interdepartmental and external collaborations were foundational principals to proactively address complex social and care related situations. Our objective to conduct a workshop is to share the preliminary use of the Situation, Background, Assessment, Recommendation (SBAR) tool and apply the framework in a population health context to demonstrate the reciprocal benefits when used to assess deep-rooted challenges impacted by health inequities. Through interactive “hands-on” experience, we want to leverage the knowledge and expertise from participants around the globe to learn how they apply Asset Based Community Development strategies to address the social determinants of health in group case scenarios. The key is to demonstrate how utilizing this framework encourages upstream collaborations and informs cost-effective resource supports that advocate for equitable solutions for aging at home. This 60-minute interactive workshop is best suited for seniors, caregivers, and professionals who live and work in the community support or home care sectors. Participants will be introduced to the vision of the SHWV and an overview of the impact the SBAR framework contributes to reducing caregiver strain and improved client experiences (15 minutes). In small groups, participants will be provided with case scenarios and the SBAR to guide their recommendations and actions that best support the caregiving-dyad and aging at home (20 minutes). Participants will share their insights and debrief on the innovative ways different regions and organizations overcome challenges and address service inequities for vulnerable seniors (15 minutes). Closing remarks would follow (10 minutes).
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".