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870 Contextualised knowledge synthesis for local stakeholders in ohs

2018· article· en· W2799704748 on OpenAlexaffabout
Emma Irvin, Stephen Bornstein, Kimberley Cullen, Amanda Butt, Dwayne Van Eerd, Leslie Johnson, Steven Passmore, Ron Saunders

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovative Approaches in Technology and Social Development
Canadian institutionsMemorial University of NewfoundlandUniversity of ManitobaInstitute for Work & Health
Fundersnot available
KeywordsComputer scienceKnowledge managementProcess managementData scienceBusiness

Abstract

fetched live from OpenAlex

<h3>Introduction</h3> Effective decision-making in occupational health and safety (OHS) requires having up-to-date evidence on what works from the best available research. However, the research literature does not typically take into account how implementation may be constrained by the demographic, economic or resource context of a jurisdiction/region that is considering action. Evidence-informed practices and policies need to be made based not only on an understanding of ‘what works’, but also on an understanding of ‘what will work here’. Our objective was to develop and test an innovative methodology for synthesising and contextualising current scientific knowledge in occupational health and safety. <h3>Methods</h3> The teams combined methods used by the ‘Contextualised Health Research Synthesis Program’ (CHRSP) at the Newfoundland and Labrador Centre for Applied Health Research (NLCAHR) with techniques for systematic reviewing and reporting pioneered by the Systematic Review Program at IWH. In our pilot testing we collected data about important contextual factors from key stakeholders through interviews or focus groups. <h3>Result</h3> The method we developed describes a variety of synthesis methods that can be used by researchers and stakeholders for evidence-based decision-making. In addition, we describe the process of gathering contextual information from key stakeholders. In one example related to depression in the workplace, we found that contextual factors of geography, industry/workplace, safey culture were important for stakeholders to consider in implementing evidence-based interventions. <h3>Discussion</h3> The project resulted in the creation of <i>Evidence in Context Occupational Health and Safety Operational Handbook</i> and an updated systematic review <i>Managing Depression in the Workplace</i> that was contextualised for the province of Manitoba (http://www.iwh.on.ca/systematic-reviews). The methodology has been transferred to end users in three Canadian provinces to date, Newfoundland and Labrador, Ontario and Manitoba. The handbook is a practical and relatively inexpensive way for OHS stakeholders to synthesise and contextualise evidence for decision making.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.767
Threshold uncertainty score0.488

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.105
GPT teacher head0.283
Teacher spread0.178 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
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".

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Citations0
Published2018
Admission routes2
Has abstractyes

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