A research impact model for work and health
Bibliographic record
Abstract
Research organizations, governments and funding agencies are increasingly interested in the impact of research beyond academia. While a growing literature describes research impacts in healthcare and health services, little has focused on occupational health and safety research. This article describes a research impact model that has been in use for over a decade. The model was developed to track and describe the impact of research conducted by a mid-sized institute that focuses on work and health. Model development was informed by existing models, with the goal of contextualizing the institute's case studies describing three types of research impact: evidence of the diffusion of research; evidence of research informing decision-making; and evidence of societal impact. A logic model describes research actions and outcomes, as well as key audiences and knowledge transfer approaches. A unique element is its indication of the level of difficulty in determining types of impact. The model compares well with current research impact models developed or used in healthcare and health services research, and it has been useful in guiding a mid-sized research organization's process for tracking and describing the impact of its research. It may be useful to other small and mid-sized research organizations that focus on workplace health and safety.
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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 | 0.030 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.022 | 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".