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Record W3113404221 · doi:10.4103/cjrm.cjrm_103_19

Integration of care in Northern Ontario: Rural health hubs and the patient medical home concept

2020· article· en· W3113404221 on OpenAlexaffvenueabout
Sarah Newbery, Josée Malette

Bibliographic record

VenueCanadian Journal of Rural Medicine · 2020
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsNOSM University
Fundersnot available
KeywordsHealth carePrimary carePrimary health careRural areaNursingPhonePolitical scienceHealthcare systemRural healthMedicineFamily medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: Primary care reform in Ontario that provides accessible, comprehensive patient-centred care has been a work in progress for more than a decade. With the recent emergence of Ontario Health Teams and the conclusion of the Rural Health Hub (RHH) pilot project, insight into the philosophy, culture and expectations of rural and remote centres with regard to primary care delivery is required. The concept of the patient medical home (PMH) and the RHH offers frameworks that emphasise positive attributes towards quality care systems - continuity, accessibility, comprehensiveness and localisation of services and funding for system efficiency. METHODS: The application of these frameworks to rural and remote centres was explored via semi-directed face-to-face and phone interviews with physicians, patients and healthcare administrators at six rural centres in Northern Ontario. RESULTS: Continuity of care, local integration and healthcare culture reform were cited by participants as the most important aspects of optimisation of primary care in their environments. CONCLUSION: These concepts support the RHH and PMH models and their further implementation as part of healthcare system transformation in Northern Ontario.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.330
Threshold uncertainty score0.664

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.010
Scholarly communication0.0030.002
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.026
GPT teacher head0.344
Teacher spread0.318 · 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 designQualitative
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

Citations5
Published2020
Admission routes3
Has abstractyes

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