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Record W3046909777 · doi:10.1016/j.jamda.2020.07.038

Finding the Right Balance: An Evidence-Informed Guidance Document to Support the Re-Opening of Canadian Nursing Homes to Family Caregivers and Visitors during the Coronavirus Disease 2019 Pandemic

2020· article· en· W3046909777 on OpenAlexaffabout
Nathan M. Stall, Jennie Johnstone, Allison McGeer, Misha Dhuper, Julie Dunning, Samir K. Sinha

Bibliographic record

VenueJournal of the American Medical Directors Association · 2020
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsPublic Health OntarioSinai Health SystemWomen's College HospitalUniversity of TorontoToronto Metropolitan UniversityUniversity Health Network
Fundersnot available
KeywordsVisitor patternPandemicMedicineFamily caregiversNursingCoronavirus disease 2019 (COVID-19)Caregiver burdenDiseaseDementiaInfectious disease (medical specialty)

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)0.090
metaresearch head score (Gemma)0.306
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.934
Threshold uncertainty score0.544

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0900.306
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0050.008
Bibliometrics0.0090.005
Science and technology studies0.0150.007
Scholarly communication0.0170.007
Open science0.0160.013
Research integrity0.0610.040
Insufficient payload (model declined to judge)0.0130.006

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 head0.038
GPT teacher head0.398
Teacher spread0.361 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations81
Published2020
Admission routes2
Has abstractno

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Same venueJournal of the American Medical Directors AssociationSame topicGeriatric Care and Nursing HomesFrench-language works237,207