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Record W2883256303 · doi:10.12927/hcq.2018.25524

Using an Evidence-Informed Framework and a Self-Assessment Tool to Drive Priority Setting and Action toward Senior-Friendly Care

2018· article· en· W2883256303 on OpenAlexaffabout
Ada Tsang, Ken Wong, David P. Ryan, Marlene Awad, Barbara Liu

Bibliographic record

VenueHealthcare Quarterly · 2018
Typearticle
Languageen
FieldMedicine
TopicClinical practice guidelines implementation
Canadian institutionsSunnybrook Health Science CentreUniversity of TorontoSunnybrook Hospital
Fundersnot available
KeywordsGeneral partnershipBest practiceQuality (philosophy)NursingIdentification (biology)Health careBusinessAction (physics)Public relationsMedical educationMedicineProcess managementPsychologyPolitical scienceFinance

Abstract

fetched live from OpenAlex

Since 2011, Ontario hospitals have been engaged in the Senior Friendly Hospital (SFH) Strategy led by the Regional Geriatric Program (RGP) of Toronto, in partnership with Ontario's Local Health Integration Networks (LHINs) and the RGPs of Ontario. Using the SFH Framework as a foundation, improvement in Ontario hospitals has been driven by the identification of common priorities, sharing of resources and best practices, and the development of quality indicators. We document this progress through a second environmental scan of hospitals conducted in 2014 that highlights significant advancement in all five domains of the SFH Framework. Key learnings will be shared on how this framework and its self-assessment tool helped drive practice change.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.809
Threshold uncertainty score0.863

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.195
GPT teacher head0.547
Teacher spread0.352 · 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

Citations5
Published2018
Admission routes2
Has abstractyes

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