Decreasing occupational injury and disability: The convergence of systems theory, knowledge transfer and action research
Bibliographic record
Abstract
Many work injuries and their associated disabilities are preventable, but effective prevention requires coordinated action by multiple stakeholders. In trying to achieve coordinated action occupational health practitioners can learn valuable lessons from systems theory, knowledge transfer and action research. Systems theory provides a broad view of the factors leading to injury and disability and a means to refocus stakeholder energies from mutual blaming to effective strategies for system change. Experiences from knowledge transfer will help adopt a stakeholder-centered approach that will facilitate the concrete application of the best and most current occupational health knowledge. Action research is a methodology endorsed by the World Health Organization and the US Centers for Disease Control, which provide methods for successfully engaging stakeholders needed to attain sustainable change. By combining concepts from the three fields we propose MAPAC (Mobilize, Assess, Plan, Act, Check), a five-step framework for developing projects aimed at decreasing occupational injury and disability. Although most practitioners would be familiar with some of the concepts, we believe an explicit framework linked to transferable knowledge from these diverse fields can help design and implement effective programs. We provide examples of model application in workers compensation and in the healthcare workplace.
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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 | 0.094 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.015 | 0.008 |
| Science and technology studies | 0.006 | 0.070 |
| Scholarly communication | 0.030 | 0.034 |
| Open science | 0.005 | 0.022 |
| Research integrity | 0.012 | 0.010 |
| 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".