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Record W2736427860 · doi:10.1080/14773996.2017.1356544

Occupational safety and health knowledge users’ perspectives about research use

2017· article· en· W2736427860 on OpenAlexafffund
Dwayne Van Eerd, Siobhan Cardoso, Emma Irvin, Ron Saunders, Trevor King, Sara Macdonald

Bibliographic record

VenuePolicy and Practice in Health and Safety · 2017
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsInstitute for Work & Health
FundersWorkplace Safety and Insurance Board
KeywordsCredibilityFocus groupOccupational safety and healthQualitative researchApplied psychologyCoachingPsychologySample (material)Poison controlMedical educationPublic relationsWork (physics)Human factors and ergonomicsSuicide preventionEngineeringMedicineEnvironmental healthPolitical scienceBusinessMarketingSociology

Abstract

fetched live from OpenAlex

Using research evidence in decision-making requires skill, time and resources. Our objective was to examine experiences and perspectives related to research use among occupational safety and health (OSH) knowledge users (KU). This was a qualitative study investigating how research was acquired, assessed, adapted and applied in decision-making. A purposive sample of OSH KU was invited to complete an online survey and/or participate in interviews or focus groups. Most OSH KU indicated using research evidence was important. KU reported having skills and motivation to find and evaluate research. KU also reported sharing evidence with a variety of audiences. Time and resources were consistently noted as barriers. Credibility was an overarching theme as KU wanted to use credible research and noted their own credibility relied on using research in their work. KU were creative in using research in their OSH roles. More research with a broader audience of OSH KU is needed.

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)0.035
metaresearch head score (Gemma)0.054
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.965
Threshold uncertainty score0.186

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0350.054
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0070.008
Scholarly communication0.0100.008
Open science0.0010.008
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.412
GPT teacher head0.641
Teacher spread0.228 · 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 designQualitative
DomainEvaluation
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

Citations12
Published2017
Admission routes2
Has abstractyes

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