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Record W4309765424 · doi:10.1177/09720634221128099

Expectations of the Ontario Healthcare System following the Implementation of Ontario Health Teams

2022· article· en· W4309765424 on OpenAlexaffabout
Hussain Ali Naqvi

Bibliographic record

VenueJournal of Health Management · 2022
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsHealth careIntegrated careGuidelineNursingHealthcare serviceBusinessMedicinePsychologyPolitical science

Abstract

fetched live from OpenAlex

With the arrival of Ontario Health Teams (OHTs), healthcare providers, clinicians and patients seek to witness the efficacy of an integrated care model. OHTs are built on the concept of healthcare integration, coordinated care, shared fiscal and clinical accountabilities between multiple healthcare service providers, as well as bridging the gaps between the clinical, social and health promotional aspects of care delivery. This meta-narrative review seeks to examine, compare and determine the efficacy of the integrated care model using cross-sectional studies from around the world to see how integrated care effects health related outcomes. The efficacy of the model will be determined by evaluating the abilities of other integrated care models to reduce healthcare expenditures, improve coordination of care between healthcare service providers, bolster patient satisfaction and health outcomes, minimise emergency and life-threatening cases, lower emergency hospital admission rates as well as provide a comprehensive set of healthcare services including biomedical, mental and social supports. For future applications, this study could be used as a guideline to highlight areas of improvement in integrated care models, as well as to evaluate benefits of existing models and determine best approaches forward.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.261
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.044
GPT teacher head0.417
Teacher spread0.373 · 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 teacher head, not a consensus.

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

Citations4
Published2022
Admission routes2
Has abstractyes

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