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Record W2030059539 · doi:10.1016/j.hcmf.2010.01.001

The Seniors Health Research Transfer Network Knowledge Network Model: System-Wide Implementation for Health and Healthcare of Seniors

2010· article· en· W2030059539 on OpenAlexafffundabout
Larry W. Chambers, Deirdre Luesby, Catherine Brookman, Megan Harris, Elizabeth Lusk

Bibliographic record

VenueHealthcare Management Forum · 2010
Typearticle
Languageen
FieldHealth Professions
TopicHealth Sciences Research and Education
Canadian institutionsAlzheimer Society of CanadaÉlisabeth Bruyère Hospital
FundersHealth CanadaAlzheimer SocietyCanadian Health Services Research Foundation
KeywordsHealth careKnowledge transferBusinessHealth promotionFace (sociological concept)NursingHealthcare serviceService (business)Promotion (chess)Knowledge managementPublic relationsMedicineMarketingPublic healthPolitical scienceSociologyComputer sciencePolitics

Abstract

fetched live from OpenAlex

The Ontario Seniors Health Research Transfer Network (SHRTN) aims to improve the health of older adults through increasing the knowledge capacity of 850 community care agencies and 620 long-term care homes. The SHRTN includes caregivers, researchers, policy makers, administrators, educators, and organizations. The SHRTN comprises communities of practice, a library service, a network of 7 research institutes, and local implementation teams. The SHRTN combines face-to-face meetings with information technology to promote change at the client care level in organizational and provincial policies and in the promotion of health services research.

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)0.028
metaresearch head score (Gemma)0.000
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.906
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0280.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0120.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.151
GPT teacher head0.529
Teacher spread0.378 · 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 designNot applicable
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

Citations7
Published2010
Admission routes3
Has abstractyes

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