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Record W2134829115 · doi:10.1186/1745-6673-4-15

Pandemic influenza: implications for occupational medicine

2009· article· en· W2134829115 on OpenAlexaff
W. Shane Journeay, Matthew D. Burnstein

Bibliographic record

VenueJournal of Occupational Medicine and Toxicology · 2009
Typearticle
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsBell (Canada)Dalhousie University
Fundersnot available
KeywordsInfluenza A virus subtype H5N1PandemicMedicineInfluenza pandemicPublic healthOccupational medicineHealth careAlternative medicineInfection controlFamily medicineCoronavirus disease 2019 (COVID-19)NursingEnvironmental healthIntensive care medicineOccupational exposurePathologyImmunologyInfectious disease (medical specialty)Economic growthDisease

Abstract

fetched live from OpenAlex

This article reviews the biological and occupational medicine literature related to H5N1 pandemic influenza and its impact on infection control, cost and business continuity in settings outside the health care community. The literature on H5N1 biology is reviewed including the treatment and infection control mechanisms as they pertain to occupational medicine. Planning activity for the potential arrival of pandemic avian influenza is growing rapidly. Much has been published on the molecular biology of H5N1 but there remains a paucity of literature on the occupational medicine impacts to organizations. This review summarizes some of the basic science surrounding H5N1 influenza and raises some key concerns in pandemic planning for the occupational medicine professional. Workplaces other than health care settings will be impacted greatly by an H5N1 pandemic and the occupational physician will play an essential role in corporate preparation, response, and business continuity strategies.

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)0.002
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0050.002
Insufficient payload (model declined to judge)0.0140.002

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 head0.317
GPT teacher head0.527
Teacher spread0.210 · 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
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
Published2009
Admission routes1
Has abstractyes

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