Understanding knowledge transfer in an ergonomics intervention at a poultry processing plant
Bibliographic record
Abstract
This case study reviews the knowledge transfer (KT) process of implementing a knife sharpening and steeling program into a poultry processing plant via a participatory ergonomics intervention. This ergonomics intervention required stakeholder participation at the company level to move a 'train-the-trainer' program, developed in Québec, Canada, into action on the plant's deboning line. Communications and exchanges with key stakeholders, as well as changes in steeling and production behaviours were recorded. The intervention was assumed to be at least partially successful because positive changes in work operations occurred. Ergonomic-related changes such as those documented have been cited in the academic literature as beneficial to worker health. However, several components cited in literature that are associated with a successful participatory ergonomics intervention were not attained during the project. A Dynamic Knowledge Transfer Model was used to identify KT issues that impacted on the success of train-the-trainer program. A debriefing analysis reveals that a failure to consider key participatory ergonomics factors necessary for success were related to capacity deficits in the knowledge dissemination strategy.
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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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".