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Record W4385754698 · doi:10.32920/23935236.v1

Strengthening N95 Filtering Facepiece Respirator Protection Programs by Evaluating the Contribution of Each of the Program Elements

2023· preprint· en· W4385754698 on OpenAlexafffund
Quinn Danyluk, Chun‐Yip Hon, George Astrakianakis, Elizabeth Bryce, Bob Janssen, Annalee Yassi

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicInfection Control and Ventilation
Canadian institutionsWorkers Compensation Board of British ColumbiaFraser HealthUniversity of British ColumbiaVancouver Coastal Health
FundersWorkSafeBC
KeywordsRespiratorPreparednessHealth careCoronavirus disease 2019 (COVID-19)Personal protective equipmentBusinessMedical emergencyMedicineInfectious disease (medical specialty)Political science

Abstract

fetched live from OpenAlex

N95 filtering facepiece respirators are used to protect workers from harmful airborne contaminants in the health care setting. Workplaces where respirators are deemed necessary to protect workers from airborne hazards are required to implement a respiratory protection program. This project aimed to strengthen N95 respirator protection programs and aid in better delivery of organizational pandemic preparedness plans Workplaces where respirators are deemed necessary to protect workers from airborne hazards are required to implement a respiratory protection program (RPP). This research evaluated the contribution of each component of an RPP in a healthcare setting. The project focused on N95 filtering facepiece respirators used to protect healthcare workers from harmful airborne contaminants and infectious disease

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)0.012
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.097
GPT teacher head0.381
Teacher spread0.285 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations4
Published2023
Admission routes2
Has abstractyes

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