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Record W2618011856 · doi:10.12927/hcq.2017.25142

Healthcare for the Aging Citizen and the Aging Citizen for Healthcare: Involving Patient Advisors in Elder-Friendly Care Improvement

2017· article· en· W2618011856 on OpenAlexafffundabout
Jennifer Verma, Patricia OʼConnor, Jerold Hodge, Howard Abrams, Jocelyn Bennett, Samir K. Sinha

Bibliographic record

VenueHealthcare Quarterly · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsCanadian Forces CollegeUniversity Health NetworkMcGill UniversitySinai Health SystemCanadian Foundation for Healthcare Improvement
FundersCanadian Frailty NetworkCanadian Foundation for Healthcare Improvement
KeywordsHealth careBest practicePopulation ageingGeneral partnershipInclusion (mineral)NursingPopulationBusinessPublic relationsMedicinePsychologyPolitical science

Abstract

fetched live from OpenAlex

With an aging population and a healthcare system that is overly reliant on providing expensive and sometimes problematic hospital-based care for older Canadians, driving improvements that promote elder-friendly care has never been more critical. The Acute Care for Elders (ACE) Strategy at Toronto's Mount Sinai Hospital is the focus of a pan-Canadian collaborative delivered by the Canadian Foundation for Healthcare Improvement in partnership with the Canadian Frailty Network. The intent is to spread the ACE Strategy's elder-friendly models of care and practices to 18 participating healthcare delivery organizations. A key element of the ACE Collaborative is the inclusion of patient advisors as members of the 18 teams. This article considers the development of elder-friendly care models and practices, with lessons for patient advisors and organizations on the necessary skill-mix, as well as lessons for providers and managers on ways to more effectively engage patient advisors in health system improvement to better serve an aging population.

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)0.013
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.797
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0010.001
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 head0.140
GPT teacher head0.400
Teacher spread0.260 · 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 designTheoretical or conceptual
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

Citations13
Published2017
Admission routes3
Has abstractyes

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