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Record W4404426797 · doi:10.1177/08404704241299341

From perpetual pilots to sustainable transformation: Scaling up geriatric care

2024· article· en· W4404426797 on OpenAlexaffabout
Jacobi Elliott, George Heckman, Humberto Omaña, Bradley Hiebert, Sheri-Lynn Kane

Bibliographic record

VenueHealthcare Management Forum · 2024
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsSt Joseph's Health CareLawson Health Research InstituteWestern University
Fundersnot available
KeywordsPopulation ageingHealth careContext (archaeology)Scale (ratio)Healthcare systemGerontologyNursingBusinessPopulationPublic relationsPsychologyMedicineEconomic growthPolitical scienceEnvironmental healthGeography

Abstract

fetched live from OpenAlex

With an ageing population, there is an increasing need to focus on the care of older adults, particularly those who are more medically complex. Frail older adults are more likely to require care from multiple providers across multiple settings. It is well recognized that the current Canadian healthcare system is not well-designed for this complex population. To address the health system challenges, health leaders are rapidly developing and implementing programs to better support the ageing population. Unfortunately, this often means that organizations are implementing and scaling health and social care programs with limited evidence or understanding of the specific context in which it was implemented. Drawing on regional experiences, this article will explore challenges and offer solutions related to the implementation, spread, and scale of healthcare programs for older adults.

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)0.052
metaresearch head score (Gemma)0.047
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.052
Threshold uncertainty score0.276

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0520.047
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0180.021
Scholarly communication0.0160.020
Open science0.0060.043
Research integrity0.0060.014
Insufficient payload (model declined to judge)0.0080.002

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 head0.015
GPT teacher head0.306
Teacher spread0.291 · 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 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

Citations1
Published2024
Admission routes2
Has abstractyes

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