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Record W4385754725 · doi:10.32920/23935254

Protecting the faces of health care workers: knowledge gaps and research priorities for effective protection against occupationally-acquired respiratory infectious diseases

2023· preprint· en· W4385754725 on OpenAlexaboutno aff
Annalee Yassi, Elizabeth Bryce, David H. Moore, Robert Janssen, Ray Copes, Karen H. Bartlett, Mark Fitzgerald, Mark Gilbert, Philip Bigelow, Quinn Danyluk, Bruce Gamage, Chun‐Yip Hon, Tom Perry, Sharon Saunders, Laurence Svirchev, Ron J. Thiessen

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsnot available
FundersStrong
KeywordsHealth careFoundation (evidence)MedicineEnvironmental healthBusinessIntensive care medicineMedical emergencyPolitical scienceLaw

Abstract

fetched live from OpenAlex

The report, funded by the Change Foundation in Ontario, identifies several priorities that require further attention to ensure the safety of healthcare workers and how best to implement the necessary changes.

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)0.021
metaresearch head score (Gemma)0.048
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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.277
Threshold uncertainty score0.550

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.048
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.003
Science and technology studies0.0040.006
Scholarly communication0.0100.009
Open science0.0020.004
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0180.002

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 head0.248
GPT teacher head0.541
Teacher spread0.292 · 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
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

Citations10
Published2023
Admission routes1
Has abstractyes

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