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Record W4390942447 · doi:10.5334/ijic.icic23501

Neighbourhood Care Teams: Integrating Health Care and Social Services for Seniors in Toronto Community Housing

2023· article· en· W4390942447 on OpenAlexaffabout
Jocelyn Charles, Einat Danieli, Kiara Fine, Jaipreet Kohli, Stacy Landau, Jagger Smith, Naomi Ziegler

Bibliographic record

VenueInternational Journal of Integrated Care · 2023
Typearticle
Languageen
FieldHealth Professions
TopicAging, Elder Care, and Social Issues
Canadian institutionsPublic Health OntarioBaycrest HospitalCanada Mortgage and Housing CorporationSunnybrook Health Science Centre
Fundersnot available
KeywordsIntegrated careNursingService providerHealth careBusinessNeighbourhood (mathematics)Service (business)MedicinePublic relationsMarketingPolitical science

Abstract

fetched live from OpenAlex

Toronto Seniors Housing Corporation (TSHC), owned by the City of Toronto, provides housing for 15,000 low-income seniors in 83 seniors-designated buildings across Toronto. The North Toronto Ontario Health Team (including primary care, hospital, community and home-care) partnered with one of the TSHC buildings in North Toronto to develop and implement a Neighbourhood Care Team (NCT) model to support TSHC’s Integrated Service Model to address tenants' health and social needs, co-designed with the tenants. The goal of the NCT is to provide an integrated model of care that is accountable to meeting the needs of people living within a specific neighbourhood so that people experience one system that provides simple access to service, and care that is coordinated with streamlined communication of health care providers. The NCT objectives include: Increasing primary care provider connections Increasing mental health & addictions care access and support options Increase Digital Health access and literacy to support primary care and specialist access, reduce social isolation and increase wellness Reduce avoidable ED and hospital use. The service design is guided by a co-design process with the tenants as follows: Door to door survey to engage tenants in identifying their barriers and the services and supports most meaningful to them Eliciting and voting on key education and support initiatives at an influenza vaccination clinic Communication back to tenants regarding the results of the survey and how the strategies/activities planned for the building have been prioritized based on their feedback. Multi-organization Education Fair focusing on the top issues addressed during the vaccination clinic survey which was well attended Regular educational sessions in response to tenant interest, combined with a self-screening component to help link the information to a concrete service/intervention to promote better health. Providing translation support to enable access and engagement by tenants from a variety of cultural backgrounds. Ongoing commitment to continue and co-design services and elicit tenants’ feedback. The team has worked to design structures to strengthen coordination and collaboration among the various delivery partners: Multi-organizational bi-weekly huddles to discuss residents identified with unmet needs (with consent or anonymized without consent) and identify options for improving their access to health care/social services and respond to their needs in a timely manner Designed pathways for ensuring attachment to primary care, access to primary care and specialist support, access to home care services and assistance with social determinants of health Established mechanisms to obtain informed consent and enable information sharing between delivery partners. Multi-modality tenant engagement to tailor services and supports to a TSHC building has led to increased involvement by tenants and a growing interest by tenants in strategies to improve their health and social inclusion. Cross-sector collaboration is an efficient and effective way to establish needs-based integration of health and social care services in this setting. Strong leadership as well as co-developed processes, frequent building meetings and cross-sector huddles were effective ways of sharing innovative ways of meeting needs with limited resources.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.502
Threshold uncertainty score0.990

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0050.001
Scholarly communication0.0020.001
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.026
GPT teacher head0.414
Teacher spread0.388 · 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 designObservational
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".

Quick stats

Citations0
Published2023
Admission routes2
Has abstractyes

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