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Record W4413358705 · doi:10.5334/ijic.nacic24124

Offering Hyper-local Community Health and Information Fairs Through an Ontario Health Team to Provide Low-Barrier Access to Care.

2025· article· en· W4413358705 on OpenAlexaboutno aff
Cassandra Kwok

Bibliographic record

VenueInternational Journal of Integrated Care · 2025
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsHealth careCommunity healthNursingBusinessMedicinePublic relationsPublic healthPolitical science

Abstract

fetched live from OpenAlex

Background: Pre-existing care gaps such as cancer screening, vaccines, chronic disease management, and access to health and community support services have been exacerbated during the pandemic in neighbourhoods that have health inequalities impacted by social determinants of health. We have developed an innovative, person and community-centered approach to increase access to health and care resources in equity-deserving neighbourhoods in North York, Ontario, Canada through Community Health and Information Fairs (CHIFs). Approach: North York Toronto Health Partners Ontario Health Team has worked collaboratively in partnership with local community health centers, the North York Family Health Team, community support services, community ambassadors, and physicians to organize and execute Community Health and Information Fairs (CHIFs) to increase community access to health and care resources, particularly for those without a dedicated primary care provider. The goal of the CHIFs is to improve community health by providing low-barrier access to care in equity-deserving neighbourhoods. Offerings at the CHIFs have been designed based on input from community partners and analysis of local health reporting data such as cancer screening rates and numbers of unattached patients. Community ambassadors, who are trusted and known members of the local community, have been trained in cancer screening guidelines and engaged to educate community members, encourage them to come to the CHIFs to access cancer screening tests and blood pressure/blood sugar checks, and help connect people to local community health centres and/or link them to Health Care Connect Ontario to gain access to a primary care provider. The CHIFs are held at times, in locations, and in ways guided by information from the community partners and ambassadors in order to provide culturally sensitive and informed care. Results: Over the past two years, we have held 20 CHIFs, completed 67 cancer screenings, 0 blood pressure readings, and blood glucose readings. At least 30% of the attendees reported not currently having a primary care provider. The multi-disciplinary CHIFs have provided unattached and equity-deserving patients access to primary care providers offering cancer preventive education and screening (cervical, breast and colorectal), health promotion and prevention education, and referrals to community support services and agencies including mental health services. On-the-spot blood pressure and blood sugar checks have supported individuals with their risk of illness and helped them to navigate the appropriate health providers or community resources needed to manage their chronic diseases and address other health requirements. Implications: By leveraging existing community resources, collaborating with local partners organizations, and partnering with community ambassadors, we have been able to support the preventative care and chronic disease management needs of equity-deserving people experiencing barriers to accessing primary care through the innovative person and community-centered strategy of organizing CHIFs. Community ambassadors in particular have been crucial to the success of this endeavour by providing insight into the specific needs and interests of local communities, as well as building trust, engagement, and mobilizing local support for the CHIFs.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.237
Threshold uncertainty score0.477

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0070.001
Scholarly communication0.0010.001
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0590.003

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 head0.038
GPT teacher head0.431
Teacher spread0.393 · 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 designNot applicable
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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