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Health and Safety

2015· other· en· W4251480517 on OpenAlexaff
Nick Turner, Paul M. Dueck

Bibliographic record

VenueWiley Encyclopedia of Management · 2015
Typeother
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsOccupational safety and healthWork (physics)Occupational health nursingCompensation (psychology)Human resource managementBusinessAffect (linguistics)Organizational safetyEffective safety trainingPsychologyRisk analysis (engineering)Environmental healthKnowledge managementOrganizational performancePublic healthMedicineEngineeringNursingMarketingComputer scienceOrganizational behavior and human resourcesHealth promotionSocial psychology

Abstract

fetched live from OpenAlex

Abstract Do human resource management ( HRM ) practices affect occupational health and safety? We first describe the prevalence of workplace injuries as an indicator of occupational health and safety in the United States, and then describe existing research on the relationship between HRM systems (e.g., “high‐performance work systems”) and occupational safety. This research helps to affirm evidence that some of the same mechanisms that encourage high organizational performance also have a beneficial relationship with occupational health and safety. We then focus on two particular HRM practices, namely, autonomous work design and relative compensation, detailing evidence of their effects on occupational health and safety at the individual and organizational levels of analysis.

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)0.003
metaresearch head score (Gemma)0.009
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 score0.156
Threshold uncertainty score0.521

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0020.002
Scholarly communication0.0050.002
Open science0.0010.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.1560.033

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 head0.040
GPT teacher head0.409
Teacher spread0.369 · 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

Citations2
Published2015
Admission routes1
Has abstractyes

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