P-160 IMPACTS ARISING FROM WORKPLACE HEALTH AND SAFETY RESEARCH FOR A GOVERNMENT AUTHORITY
Bibliographic record
Abstract
Abstract Introduction Based on work by the Institute for Work & Health (Canada), a research impact framework was co-developed between the Institute for Safety, Compensation and Recovery Research (ISCRR, Australia) and WorkSafe Victoria (Australia) to measure and evaluate the impact of ISCRR research on policies, procedures and decision-making. This framework has now become a guiding strategy for ISCRR’s research translation activities. Methods This impact framework focusses on three main levels of research impact: i) research dissemination and diffusion, ii) informing decision-making, and iii) contribution towards societal change. All research carried out by ISCRR researchers for WorkSafe Victoria are now assessed for research impact. Results An audit of 35 ISCRR research projects completed between 2019-22 found that 77% (n=27) had achieved some form of impact which informed decision-making. These decision-making impacts ranged from informing internal strategies, treatment options and guidelines, and further research, as well as leading to the development of programs and initiatives. Two research projects demonstrated a connection to preliminary societal level impacts improving the outcome of injured workers. For the research projects without measured decision-making impacts (n=8), these were either too early in their impact journey, or there were difficulties in obtaining research impact metrics. Discussion The majority of ISCRR research for WorkSafe Victoria directly informs decision-making processes. The ability to measure these impacts relies on a proactive approach and champion identification. Conclusion Government authorities strive to make evidence-informed decisions backed by solid research. Research impact measurement and monitoring is important to demonstrate this process and identify opportunities to further impact progression.
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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 | 0.131 | 0.216 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.020 | 0.009 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.043 | 0.004 |
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".