Decreasing occupational injury and disability: The convergence of systems theory, knowledge transfer and action research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it