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Did You Have an Impact? A Theory-Based Method for Planning and Evaluating Knowledge-Transfer and Exchange Activities in Occupational Health and Safety

2013· article· en· W1567865367 on OpenAlexaff
Desré M. Kramer, Richard Wells, Nicolette Carlan, Theresa Aversa, Philip P. Bigelow, Shane M. Dixon, Keith McMillan

Bibliographic record

VenueInternational Journal of Occupational Safety and Ergonomics · 2013
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsToronto Metropolitan UniversityUniversity of Waterloo
Fundersnot available
KeywordsTranstheoretical modelKnowledge transferKnowledge managementStakeholderOccupational safety and healthComputer sciencePsychological interventionManagement scienceRisk analysis (engineering)Process managementEngineeringBusinessMedicineNursing

Abstract

fetched live from OpenAlex

Few evaluation tools are available to assess knowledge-transfer and exchange interventions. The objective of this paper is to develop and demonstrate a theory-based knowledge-transfer and exchange method of evaluation (KEME) that synthesizes 3 theoretical frameworks: the promoting action on research implementation of health services (PARiHS) model, the transtheoretical model of change, and a model of knowledge use. It proposes a new term, keme, to mean a unit of evidence-based transferable knowledge. The usefulness of the evaluation method is demonstrated with 4 occupational health and safety knowledge transfer and exchange (KTE) implementation case studies that are based upon the analysis of over 50 pre-existing interviews. The usefulness of the evaluation model has enabled us to better understand stakeholder feedback, frame our interpretation, and perform a more comprehensive evaluation of the knowledge use outcomes of our KTE efforts.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.362
Threshold uncertainty score0.648

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.423
GPT teacher head0.648
Teacher spread0.224 · 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 designObservational
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".

Quick stats

Citations27
Published2013
Admission routes1
Has abstractyes

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