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Record W2614808491 · doi:10.7249/rr1463

Systematic Reviews for Occupational Safety and Health Questions: Resources for Evidence Synthesis

2016· book· en· W2614808491 on OpenAlexfundno aff
Susanne Hempel, Lea Xenakis, Marjorie Danz

Bibliographic record

VenueRAND Corporation eBooks · 2016
Typebook
Languageen
FieldMedicine
TopicClinical practice guidelines implementation
Canadian institutionsnot available
FundersNational Institute for Occupational Safety and HealthAgency for Healthcare Research and QualityMcMaster UniversityUniversity of PittsburghRAND Corporation
KeywordsSystematic reviewManagement scienceRisk analysis (engineering)Computer sciencePsychologyEngineering ethicsMedicineEngineeringMEDLINEPolitical science

Abstract

fetched live from OpenAlex

This report outlines the steps undertaken in a systematic review of the literature to summarize the existing evidence to answer a research or policy question with a transparent, reliable, and valid approach. It provides practical guidance to execute systematic reviews as well as considerations and available resources specific to occupational safety and health evidence synthesis.

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)0.104
metaresearch head score (Gemma)0.451
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.896
Threshold uncertainty score0.551

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1040.451
Meta-epidemiology (narrow)0.0030.005
Meta-epidemiology (broad)0.0090.005
Bibliometrics0.0440.042
Science and technology studies0.0020.003
Scholarly communication0.0110.012
Open science0.0050.011
Research integrity0.0070.007
Insufficient payload (model declined to judge)0.0570.030

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 head0.398
GPT teacher head0.498
Teacher spread0.100 · 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.

Study designNot applicable
DomainMethods
GenreMethods

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

Citations25
Published2016
Admission routes1
Has abstractyes

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