Dancing the two-step: Collaborating with intermediary organizations as research partners to help implement workplace health and safety interventions
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effect of the involvement of intermediaries who were research partners on three intervention studies. The projects crossed four sectors: manufacturing, transportation, service sector, and electrical-utilities sectors. The interventions were participative ergonomic programs. The study attempts to further our understanding of collaborative workplace-based research between researchers and intermediary organizations; to analyze this collaboration in terms of knowledge transfer; and to further our understanding of the successes and challenges with such a process. PARTICIPANTS: The intermediary organizations were provincial health and safety associations (HSAs). They have workplaces as their clients and acted as direct links between the researchers and workplaces. METHODS: Data was collected from observations, emails, research-meeting minutes, and 36 qualitative interviews. Interviewees were managers, and consultants from the collaborating associations, 17 company representatives and seven researchers. RESULTS: The article describes how the collaborations were created, the structure of the partnerships, the difficulties, the benefits, and challenges to both the researchers and intermediaries. The evidence of knowledge utilization between the researchers and HSAs was tracked as a proxy-measure of impact of this collaborative method, also called Mode 2 research. CONCLUSION: Despite the difficulties, both the researchers and the health and safety specialists agreed that the results of the research made the process worthwhile.
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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 | 0.127 | 0.191 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.005 | 0.022 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".