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Record W4288766913 · doi:10.1002/9781119887638.ch13

Risk Management

2022· other· en· W4288766913 on OpenAlexaff

Bibliographic record

Venuenot available
Typeother
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRisk managementHarassmentRisk analysis (engineering)PreparednessOccupational safety and healthPsychosocialAffect (linguistics)Applied psychologyBusinessControl (management)PsychologyNursingComputer scienceMedicinePolitical science

Abstract

fetched live from OpenAlex

Addressing occupational health risks and ensuring that they are managed should be addressed in the manner that one approaches all potential safety issues. The implementation of a mechanism for feedback and improvement should also be put in place to manage the assessed risks and aid continuous improvement over time. Psychosocial hazards can also be considered as they can affect workers’ health as a result of their perceptions and personal experiences that cover a broad range of situations that can vary from job insecurity and stress to workplace bullying or harassment. Risk assessment processes can be both quantitative and qualitative, involving experts or utilising control banding tools, and including participatory methods. Risk communication in the occupational setting is most often associated with emergency preparedness and response, communication to the public and com munication within and between responders to a potential crisis situation.

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)0.015
metaresearch head score (Gemma)0.032
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: Other · Consensus signal: Other
Teacher disagreement score0.074
Threshold uncertainty score0.246

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.032
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0030.001
Scholarly communication0.0080.005
Open science0.0040.007
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0740.025

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 head0.075
GPT teacher head0.506
Teacher spread0.431 · 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
GenreOther

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

Citations0
Published2022
Admission routes1
Has abstractyes

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