Bibliographic record
Abstract
The use of evidence by those involved in health and safety practice is at times sporadic with knowledge often kept behind paywalls and practitioners being unable to access it. There are additional problems with a gap between research within tertiary institutions being focused on single issue problems and the timing of research projects. This adds to the at times difficult, development of workplace interventions in a complex work environment. This presentation will highlight some of those issues around enabling translation of research into practice and talk about the pilot project, the Wellbeing at Work Hub. The Hub, based on a “what works” centre design was developed to take research evidence, synthesise it into a series of principles that can then be applied in practice. However, our immediate learnings from the pilot are that of credibility, thus any items shared have to be tested and validated through a systematic approach. While the hub pilot has been completed and we continue to build the hub, there is a need for further engagement involving stakeholders, industry, researchers and practitioners to identify the research questions that need to be addressed. The development of a research/practitioner network at VUW is ongoing and taking the example from Canada, where networks work together to co-design research which is both useful and doable. Working together we can build that evidence base, drawing from international research and building our own local research knowledge; with the aim of influencing and improving practice.
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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 | 0.146 | 0.222 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.025 | 0.025 |
| Open science | 0.005 | 0.024 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.013 | 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".