MétaCan
Menu
Back to cohort
Record W2523171936 · doi:10.1177/1541931215591118

Potential Health Care Worker Contamination from Ebola Virus Disease during Personal Protective Equipment Removal and Disposal

2015· article· en· W2523171936 on OpenAlexafffund
Greg Hallihan, Jan M. Davies, Justin Baers, Katelyn Wiley, Jaime Kaufman, John Conly, Jeff K. Caird

Bibliographic record

VenueProceedings of the Human Factors and Ergonomics Society Annual Meeting · 2015
Typearticle
Languageen
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsUniversity of Calgary
FundersCenters for Disease Control and PreventionAlberta Health Services
KeywordsPersonal protective equipmentEbola virusHealth careMedicineBiomedical wasteMedical emergencyInfection controlEnvironmental healthCoronavirus disease 2019 (COVID-19)DiseaseInfectious disease (medical specialty)SurgeryPathology

Abstract

fetched live from OpenAlex

The transmission of Ebola Virus (EV) to health care workers (HCW) has been documented in highly-resourced care settings, even with HCW use of personal protective equipment (PPE). This research describes an observational study involving simulated Ebola Virus Disease (EVD) patient scenarios in four tertiary acute care centers. Researchers recorded and analyzed audiovisual data to identify instances of potential HCW EV contamination. Video-analysis was based on a coding taxonomy developed in collaboration with Infection Prevention and Control (IPC) professionals. The analysis focused on events and actions associated with potential HCW contamination during doffing and PPE disposal, and contributing system factors. The events and actions identified included out-of-sync doffing teams, HCW deviations from doffing protocols, and improper disposal of doffed PPE including the compression of PPE in biomedical waste containers containing potentially contaminated sharps. These observations are discussed along with recommendations for the re-design of doffing procedural aids and waste disposal.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.233
Threshold uncertainty score0.916

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.026
GPT teacher head0.307
Teacher spread0.280 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations7
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueProceedings of the Human Factors and Ergonomics Society Annual MeetingSame topicDisaster Response and ManagementFrench-language works237,207