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Record W4409337106 · doi:10.5334/ijic.icic24241

Using Integrated Care Teams to Improve Access to Primary Care

2025· article· en· W4409337106 on OpenAlexaboutno aff
Susan Joyce, Jocelyn Charles, Kitty Liu, Jagger Smith, Holly Opara

Bibliographic record

VenueInternational Journal of Integrated Care · 2025
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsnot available
Fundersnot available
KeywordsPrimary careIntegrated careNursingProcess managementKnowledge managementBusinessMedicineHealth careComputer scienceFamily medicinePolitical science

Abstract

fetched live from OpenAlex

The City of Toronto is facing unprecedented challenges in meeting the health care needs of its residents due to a significant reduction in the number of family physicians providing comprehensive primary care. Many family doctors are reducing their practices or retiring early due to burn-out with a high administrative workload or because of high overhead in a rapidly growing city. In addition, fewer new graduates are practicing comprehensive family medicine. Over 20% of patients attending our local Emergency Department have no family doctor. The creation of integrated health hubs with improved access to team-based care will be effective in delivering the right care, in the right place. Leveraging data available to the North Toronto Ontario Heath Team (NT OHT), an analysis was completed to define the primary care gap in North Toronto. Using a Population Health Management approach, we learned that the unmet primary care gap in North Toronto will increase by 73% by 2026, due to population growth and fewer family doctors. Over 80% of family doctors do not have access to interprofessional healthcare providers (IHPs), the lowest in Toronto. To ensure access to primary care for all, a strategy was developed with the goal of ensuring every NT OHT resident has access to team-based primary care. This will be achieved by attracting and retaining physicians, by improving access to IHPs locally. The first phase of the strategy is the creation of two Integrated Health Hubs, based on local needs, with input from patient and family group members . These Hubs will recruit primary care providers to support 20,000 unattached patients, along with IHPs that are needed to support this population. In the future, the IHPs at the Health Hubs will be expanded to support and retain local primary care providers and their patients, who do not have access to teams. Furthermore, the IHPs will also provide care to seniors living in seniors buildings in our local community, to support earlier intervention for our senior population with rising care needs. The Integrated Health Hub will be the foundation for this model of care. In addition to team-based supports, other support services on-site will also be explored to improve access (one-stop shop experience for patients), with the potential to reduce the cost of business for primary care providers. The Hubs will also provide back-office supports for primary care as a way of reducing the administrative burden. It is anticipated that these supports will enable larger rosters per doctor, helping to reduce the access issues currently facing our system. A robust plan is in place to shape the development of Integrated Health Hubs, including Vision and Advocacy, Care Model and Operations, Space and Capital, Partnerships and Engagement. The NT OHT is working closely with all stakeholders, including patient and family advisors, primary care, academic leaders, and local community members. Hub development is a key foundation that will enable improvement in access to primary care, making practice easier for our providers, and making life healthier for the residents of the NT OHT.

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)0.005
metaresearch head score (Gemma)0.010
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 score0.148
Threshold uncertainty score0.294

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.006
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 head0.461
Teacher spread0.435 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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