Bibliographic record
Abstract
Background South Australian workers compensation data shows that young workers are 1.5 times more likely to be injured in the workplace and that more than 70 percent of injuries happen in the first year of employment. There is currently no minimum standard for teaching Workplace Health and Safety (WHS) in SA. Canadian development of the Passport to Safety programme focussing on WHS education provided the impetus for a multi-partner working group to be established in SA in September 2004. Aims/Objectives/Purpose Young workers learn about their rights and responsibilities around WHS before entering the workplace for the first time and see and hear stories from their peers. There is a minimum standard of WHS training for students about to undertake work experience and/or work placement. Methods Teachers and students took part in pre and post programme surveys regarding their WHS knowledge and a number of focus groups discussed how young people want to receive WHS information. Results/Outcome Students who took part in the survey had an average increase in knowledge of 22 percent, with Teachers averaging a 19 percent increase. Workers compensation data indicates a reduction in young worker claims from 6044 in 2005/2006 to 4268 in 2009/2010. Significance/Contribution to the Field Young workers are the political leaders, business owners and managers of the future by educating and engaging them in workplace safety we can create a generational change.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".