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Record W4413358423 · doi:10.5334/ijic.nacic24232

Integrating Care and Social Services for Low Income Seniors in Toronto Community Housing: Understanding Barriers and Exploring Solutions

2025· article· en· W4413358423 on OpenAlexaboutno aff
Einat Danieli, Jagger Smith, Fine Kiara, Naomi Ziegler, Laurie Addis, Jocelyn Charles

Bibliographic record

VenueInternational Journal of Integrated Care · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicHealthcare innovation and challenges
Canadian institutionsnot available
Fundersnot available
KeywordsLow incomeIntegrated careSocial careSociologyAging in placeGerontologyEconomic growthBusinessNursingMedicineHealth careSocioeconomicsEconomics

Abstract

fetched live from OpenAlex

The North Toronto Ontario Health Team has partnered with the Toronto Seniors Housing Corporation (TSHC) and its tenants to develop and implement a Neighbourhood Care Team model to address tenants health and social needs. Successes will be shared and creative solutions to the significant and/or ongoing barriers and challenges encountered will be discussed.Improving access to integrated care and social supports for vulnerable seniors in social housing buildings in Toronto for over four years has been very challenging, not solely due to the COVID 9 pandemic. Various challenges have included complex tenant issues, integrating workflows and accountability between organizations, and secure communication between team members and other sectors involved in tenants care and services. Tenant issues include difficulties with social determinants of health, mental health and addictions, new onset or progression of neurocognitive disorders, lack of family supports, poor access to preventive and responsive care, issues related to capacity to make personal care decisions and hoarding and pest infestation issues. Iterative interprofessional and inter-organizational collaboration with tenants has led to a wide range of initiatives and programs to address identified tenant needs. Although we have implemented many successful strategies and processes, earlier identification of tenant needs and ensuring consistent, timely and coordinated responses remain persistent challenges. As our tenant advisor stated we are missing the wake-up calls by tenants for earlier intervention to facilitate successful aging in place.We would like to engage with people who have first hand experience living or working with more vulnerable populations to share additional engagement strategies and initiatives that have been successful and any additional barriers they have experienced. This includes people living in the community and their families, primary care providers (interprofessional), community and home care providers, mental health and addictions providers, hospital staff and administrators, paramedics, and housing coordinators and administrators.Workshop 60 minutes:Introduction to the Neighbourhood Care Team model and its development - 5 minutesSuccessful engagement strategies and resulting initiatives to enhance tenant outcomes - 0 minDemonstrate barriers and challenges that limit earlier identification and coordinated responses through a case study - 0 minSmall groups - each group addressing one of the barriers/challenges and working on possible solutions based on the case study presented - 20 minGroup feedback: 0 minutesClosing: 5 minutesParticipants will listen and respond to the case study and then collaborate in small working groups to brainstorm new and creative strategies to address the challenges as well as share experiences to inform possible solutions to the barriers/challenges highlighted in the case study. We will summarize the large group list of successful engagement strategies with vulnerable populations, and the processes and pathways needed to identify and respond to their health and social needs in a timely, coordinated and consistent fashion to optimize health outcomes. We will generate a summary of the group discussion which we will share with the participants at the end of the workshop through a shared link. Following the workshop we will encourage attendees to try implement solutions recommended so that we can build knowledge on how to best integrate care and social services at this local high needs population level.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.189
Threshold uncertainty score0.380

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0140.004
Scholarly communication0.0050.003
Open science0.0020.008
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.094
GPT teacher head0.390
Teacher spread0.296 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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