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Record W3032591787 · doi:10.1097/jac.0000000000000337

COVID-19 Crisis Creates Opportunities for Community-Centered Population Health

2020· article· en· W3032591787 on OpenAlexaff
Norbert Goldfield, Robert Crittenden, Durrell J. Fox, John E. McDonough, Len M. Nichols, E. Lee Rosenthal

Bibliographic record

VenueJournal of Ambulatory Care Management · 2020
Typearticle
Languageen
FieldHealth Professions
TopicPublic Health Policies and Education
Canadian institutionsBarrick Gold (Canada)
Fundersnot available
KeywordsSpellCoronavirus disease 2019 (COVID-19)PopulationSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakHealth careKey (lock)State (computer science)Element (criminal law)MedicineEconomic growthPolitical scienceEnvironmental healthSociologyComputer scienceDiseaseEconomicsVirologyComputer securityInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Dealing with the COVID-19 coronavirus requires a coordinated transnational effort. We propose a 2-stage state-led effort that utilizes community health workers (CHWs). We spell out what is beginning to occur in states to control and suppress COVID-19. In the second stage, we suggest working with these CHWs as a key element in the next evolution of our health care system: community-centered population health.

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)0.009
metaresearch head score (Gemma)0.010
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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.017
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0060.004
Scholarly communication0.0070.005
Open science0.0020.014
Research integrity0.0060.010
Insufficient payload (model declined to judge)0.0170.002

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 head0.299
GPT teacher head0.494
Teacher spread0.195 · 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
GenreCommentary

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

Citations65
Published2020
Admission routes1
Has abstractyes

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