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Record W3216156664 · doi:10.31478/202111c

Patients, Families, and Communities COVID-19 Impact Assessment: Lessons Learned and Compelling Needs

2021· review· en· W3216156664 on OpenAlexaff
Frederick Isasi, Mary D. Naylor, David J. Skorton, David C. Grabowski, Sandra Hernández, Valerie Montgomery Rice

Bibliographic record

VenueNAM Perspectives · 2021
Typereview
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsHealth Care Foundation
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)General partnershipPandemicFunction (biology)Proposition2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Resource (disambiguation)Public relationsPolitical scienceMedicineComputer scienceVirologyDiseaseInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

The National Academies are responding to the COVID-19 pandemic. Visit our resource center >> Introduction The health system exists to serve the most fundamental need of society: people’s health and well-being. To do so effectively requires engaging people as a partnership proposition in all elements of health system structure and function. Patients, families, and […]

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)0.032
metaresearch head score (Gemma)0.051
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.032
Threshold uncertainty score0.171

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0320.051
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0040.004
Science and technology studies0.0010.001
Scholarly communication0.0050.005
Open science0.0020.004
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0090.001

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 head0.185
GPT teacher head0.516
Teacher spread0.331 · 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 designQualitative
Domainnot available
GenreReview

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

Citations67
Published2021
Admission routes1
Has abstractyes

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