Did You Have an Impact? A Theory-Based Method for Planning and Evaluating Knowledge-Transfer and Exchange Activities in Occupational Health and Safety
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.080 | 0.132 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".