Health-Bay Kiosks an innovative digital tool to bridge inequities for low-income seniors
Bibliographic record
Abstract
Background: Systemic barriers and inequities contribute to seniors and families inability to access appropriate and timely services and poor health outcomes.While digital tools have the potential to solve many of these issues, access to digital tools remains a barrier due to costs, language and digital literacy. Target populationToronto Seniors Housing Corporation (TSHC), owned by the City of Toronto, provides housing for 5,000 low-income seniors in 83 seniors-designated buildings across Toronto. 72% of tenants are between 65 to 84 years old; 6% of tenants are 85+. A large percentage of tenants (37%) do not speak English as their first language, live on their own (90%), and suffer from a combination of mental health and chronic conditions that contribute to high rates of social isolation (TSHC ISM, 2024).Many of these seniors experience financial, language and literacy barriers in accessing digital resources and devices. Many cannot afford to purchase a devices or pay for internet service. Who did you involve/ engage withThe platform empowers tenants to manage their own health and was developed in close collaboration with the tenants, community partners and stakeholders from healthcare, community and housing sectors. This involved co-design sessions with over 6o tenants to understand what tenants were interested in and found useful as well as beta testing with tenant groups in 4 different buildings, to provide feedback on and inform the platform design and utility. The approach: Health-Bay by Baycrest is a web platform geared towards senior tenants residing in Toronto Seniors Housing with the aim of providing them with access to health information and education resources; self-assessments to learn about their own well-being; access to useful resources and the ability to request support from their local Neighbourhood Care Team (NCT) members in their building in a senior user-friendly format.The platform is translated into multiple languages and can be accessed through tenants own devices such as smartphones, iPads, tablets or computers. It is also available via a physical kiosk in a central location at each building to enable equitable access for those who do not own a device or do not have access to internet in their unit.The kiosk serves as a digital door to health information and services bridging both digital and language literacy inequities.The platform was made possible through a Centre for Aging Brain Health Innovation SPARK grant and serves as an enhancement of the local care teams working within the building by enabling a digital mechanism for information flow and access to services. Results: The Health-Bay kiosks have been piloted in four Toronto Seniors Housing buildings. Initial results were promising, with an increasing number of tenants using the platform and connecting with needed clinical services over the course of the pilot. Impact: Through this presentation audience would learn about:. The Health-Bay platform and its functions2. How to co-develop digital solutions with patients and community partners3. How digital solutions can be used to support equitable access to integrated care models Next steps:Our intention is to expand and scale the platform to additional buildings to support more seniors. We will continue to develop and evaluate the platform based on tenant feedback and add additional functions and language capabilities to support more languages.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.004 |
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".