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Record W2914328904 · doi:10.12927/hcq.2018.25709

Putting a Population Health Lens to Multimorbidity in Ontario

2018· article· en· W2914328904 on OpenAlexaffvenueabout
Laura C. Rosella, Kathy Kornas

Bibliographic record

VenueHealthcare Quarterly · 2018
Typearticle
Languageen
FieldMedicine
TopicChronic Disease Management Strategies
Canadian institutionsInstitute of Population and Public HealthPublic Health Ontario
Fundersnot available
KeywordsHealth careMultimorbidityEquity (law)MedicineChronic diseasePopulationPopulation healthChronic conditionDisease managementDiseaseHealth equityHealthcare systemFamily medicineGerontologyPublic healthEnvironmental healthNursingHealth management systemAlternative medicineEconomic growthPathologyPolitical science

Abstract

fetched live from OpenAlex

Almost all Ontarians die with multimorbidity, and most accumulate more than five conditions over their lifetime. Our health system is still largely focused on specialties and treating one disease at a time - an approach that is incompatible with the healthcare needs of patients with multiple and often complex chronic conditions. This burden requires a health system that recognizes that patients will more likely live and die with multiple chronic conditions than not (i.e., multimorbidity management versus specialized care). There are important and meaningful differences in the types and numbers of conditions that patients die with. In particular, increases in the most preventable conditions are greater among the most deprived members of our society. To address the worrying trends seen here, chronic disease prevention - not only management - must be a priority, with a strong focus on health equity. Chronic disease prevention and a strong focus on equity are signatures of a population health approach. This work echoes calls for a stronger emphasis on population health in the health system.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.247
Threshold uncertainty score0.647

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.083
GPT teacher head0.381
Teacher spread0.297 · 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.

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

Citations10
Published2018
Admission routes3
Has abstractyes

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