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Record W4390944995 · doi:10.5334/ijic.icic23118

The Evolution of Ontario Health Teams: A Developmental Evaluation

2023· article· en· W4390944995 on OpenAlexaffabout
Gayathri Embuldeniya, Kaileah McKellar, Elana Commisso, Ruth Hall, Walter P. Wodchis

Bibliographic record

VenueInternational Journal of Integrated Care · 2023
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsInstitute for Clinical Evaluative SciencesInstitute of Health Services and Policy ResearchTrillium Health CentreUniversity of Toronto
Fundersnot available
KeywordsIntegrated careHealth careContext (archaeology)PopulationCorporate governanceNursingPsychologyMedical educationPublic relationsSociologyMedicinePolitical scienceBusinessGeography

Abstract

fetched live from OpenAlex

Background: Ontario, Canada’s most populous province, is no stranger to integrated care initiatives. However, none have been as ambitious and far-reaching as the Ontario Health Team (OHT) initiative, announced by its Ministry of Health (MOH) in 2019. Its eventual objective is population health management – every Ontarian will belong to an OHT providing them with seamlessly coordinated care across the hospital-community continuum. Our team conducted a Developmental Evaluation (DE) that sought to understand and guide the development of 6 OHTs over the course of a year. Methods: Six OHTs were chosen, based on their representation of a range of geographies and readiness and willingness to participate. DE employs embedded research and collaboration between researchers and participants to understand and guide development. Findings are co-constructed and shared with participants continuously. DE was paired with ethnography which employs multiple methods and prioritizes close engagement with participants over time. Data collection took place from Spring 2021-2022, led by a team of five embedded researchers. Approximately 275 OHT development meetings were observed and 30 interviews conducted. Key participants were also invited to periodically journal about their experience of working together on OHT development. While analysis was guided by existing frameworks (Context and Capabilities for Integrated Care Framework & the MOH’s 8 “building blocks”), a ground-up inductive approach was privileged. Results: We identified 9 key areas of development: a) developing vision, b) establishing governance, c) strategic planning, d) designing and implementing integrated models, e) advancing digital health, f) engaging primary care, g) partnering with patients, families and caregivers, h) establishing funding and incentive structures, and i) enhancing performance measurement, quality improvement and continuous learning. Discussion & Impact: Local Analysis of our results yielded the following insights: a) OHTs were evolving at different paces, each at a different stage of progress in relation to key areas of development, b) despite differences in evolutionary trajectory, there were shared contexts, structures and cultures that both forwarded and frustrated progress across OHTs, and c) while findings largely overlapped with the MOH’s eight building blocks for OHT maturity, there were key differences. These differences suggest the need for change management that supports the translation of macro-level expectations into local realities, while ensuring on-the-ground realities are reflected in expectations. Recommendations were generated to guide OHT development across the province. International This research contributes to scholarship on the context-bound practice of integrated care - its constant negotiation of both local and systemic contexts. It also provides an important example of how an initiative that is similar in purpose to population health and integrated care initiatives such as the Integrated Care System (ICS) in the UK and Accountable Care Organizations (ACO) in the US, may be differently implemented and experienced across an entire health system. Next Steps: While we have an understanding of OHT struggles with the system and policy environment, we know less about policymakers’ perspectives on these issues. Our next phase of research will therefore explore how policymakers respond to systemic gaps identified by OHTs and where their own concerns lie.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: yes · About a Canadian topic: yes
Qualitativelow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: yes · About a Canadian topic: yes
Observationallow
models splitAgreement compares identical category sets and study designs across arms.

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)0.094
metaresearch head score (Gemma)0.095
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 score0.354
Threshold uncertainty score0.930

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0940.095
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0100.005
Scholarly communication0.0060.004
Open science0.0040.009
Research integrity0.0010.002
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 head0.050
GPT teacher head0.437
Teacher spread0.387 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designQualitative · Observational
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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