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Record W2918196420 · doi:10.1016/j.jamda.2019.01.123

Toward Common Data Elements for International Research in Long-term Care Homes: Advancing Person-Centered Care

2019· article· en· W2918196420 on OpenAlexaff
Kirsten Corazzini, Ruth A. Anderson, Barbara J. Bowers, Charlene H. Chu, David Edvardsson, Anette Fagertun, Adam Gordon, Angela Yee Man Leung, Katherine S. McGilton, Julienne Elizabeth Meyer, Elena O. Siegel, Roy Thompson, Jing Wang, Sijia Wei, Bei Wu, Michael Lepore

Bibliographic record

VenueJournal of the American Medical Directors Association · 2019
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversity Health NetworkToronto Rehabilitation InstituteUniversity of Toronto
FundersCenter for Neuroscience Research
KeywordsOperationalizationStaffingWorkforceHealth careMedicineContext (archaeology)Long-term careMinimum Data SetNursingNursing homesPolitical science

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.306
metaresearch head score (Gemma)0.459
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.694
Threshold uncertainty score0.855

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3060.459
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0040.005
Bibliometrics0.0190.019
Science and technology studies0.0080.011
Scholarly communication0.0320.044
Open science0.0070.027
Research integrity0.0050.013
Insufficient payload (model declined to judge)0.0060.003

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 head0.103
GPT teacher head0.480
Teacher spread0.377 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designTheoretical or conceptual
DomainMethods
GenreMethods

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

Citations56
Published2019
Admission routes1
Has abstractno

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