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Record W2911823760 · doi:10.13162/hro-ors.v7i1.3413

Integrating Primary Care, Home Care, and Community Health Services in Ontario

2019· article· fr· W2911823760 on OpenAlexaffvenueabout
Sevrenne Sheppard

Bibliographic record

VenueHealth Reform Observer - Observatoire des Réformes de Santé · 2019
Typearticle
Languagefr
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsMcGill University
Fundersnot available
KeywordsLegislaturePublic administrationHealth careEquity (law)AccountabilityPolitical scienceCorporate governanceBureaucracyTransparency (behavior)Public relationsBusinessNursingMedicinePoliticsLaw

Abstract

fetched live from OpenAlex

The Patients First Act is the legislative piece of a large-scale reform in Ontario's health systems governance. Prior to 2017, home and community care in Ontario was managed by Community Care Access Centres (CCACs), which were overseen by Local Health Integration Networks (LHINs) in each region. Both the CCACs and the then provincial government had come under public criticism for inefficient spending, lack of coordination between health care providers, exacerbating existing inequalities with regards to home and community care, and adding an unnecessary layer of bureaucracy to an already strained health care system. The Patients First Act was introduced in 2016 in order to better integrate home and community care with primary care, and to improve efficiency, transparency, and continuity of care for patients. This organizational reform was achieved by abolishing the CCACs and transferring their duties to the LHINs, along with the authority to issue policy directives to other health services providers. The Patients First Act is a recent development in Ontario health systems reform, and ongoing evaluation is needed to determine the full impact of this policy. While the strengths of the Act include its focus on access and accountability for patients, there are significant gaps that remain to be addressed, including the role of LHINs in working with physicians and hospital boards, and in framing equity issues beyond geographical difference to include growing ethnocultural and linguistic diversity.

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 categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesMeta-epidemiology (narrow), Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.143
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.002
Science and technology studies0.0050.001
Scholarly communication0.0000.003
Open science0.0020.002
Research integrity0.0020.011
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 head0.036
GPT teacher head0.349
Teacher spread0.313 · 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; both teacher heads agree on what is shown here.

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

Citations3
Published2019
Admission routes3
Has abstractyes

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