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Record W3203866899 · doi:10.2196/31905

Peer Review of “SARS-CoV-2 Vaccination Uptake in a Correctional Setting: Cross-sectional Study”

2021· article· en· W3203866899 on OpenAlexvenueno aff
Benjamin A. Howell

Bibliographic record

VenueJMIRx Med · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsnot available
Fundersnot available
KeywordsCross-sectional studyCoronavirus disease 2019 (COVID-19)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakMedicineVaccinationVirologySars virusFamily medicineOutbreakPathologyInfectious disease (medical specialty)Disease

Abstract

fetched live from OpenAlex

This is a peer-review report submitted for the paper "SARS-CoV-2 Vaccination Uptake in a Correctional Setting". Round 1 Review General CommentsThis is an important manuscript [1] describing the efforts of the Rhode Island Department of Corrections (RIDOC) to roll out a vaccine program in their unified state correctional system.First, I would be careful in describing this as an "evaluation."It is a description of the rollout of the vaccine program, and I did not find any elements of an evaluation.Second, the manuscript could be much improved with increased clarity in the writing.Even as a reader who knows more about the RIDOC correctional system than the average reader, I got confused at times about what the authors were referring to.Adding more details on the RIDOC (and how it compares to other correctional systems) will aid generalizability, and also adding more details about the RIDOC vaccination program will help readers contextualize their findings.I recommend rewriting this manuscript with a more general public health audience in mind (who will likely know less about correctional systems).

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)0.020
metaresearch head score (Gemma)0.205
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.980
Threshold uncertainty score0.157

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.205
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0040.002
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0470.018

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 head0.072
GPT teacher head0.415
Teacher spread0.343 · 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.

Study designNot applicable
DomainEvaluation
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

Citations3
Published2021
Admission routes1
Has abstractyes

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