New employees accident and injury rates in Australia: A review of the literature
Bibliographic record
Abstract
According to the Australian Bureau of Statistics (ABS, 2018) every year in Australia there are more than half a million work related accidents and injuries. The financial, human and social costs of work related accidents and injuries are a major concern for not only individual workplaces but at all levels for International and National authorities. International research since 1917 has consistently demonstrated that, irrespective of age, experience and industry, the occupational group at greatest risk of accidents and injuries are those employees with less than 12 months experience in their current job role. Whilst the elevated risk for new employees has always been concerning, recent organisational developments such as globalisation and increased non-standard employment, as well as workers changing jobs more frequently have strengthened these concerns. A review of the Australian and International literature has shown that approximately 30% to 40% of new employees sustain an injury within the first year of employment. Research in Australia on this topic, however, appears to be lagging and is worthy of further attention and a stronger focus on how to remediate this global issue. Compared to other countries such as Canada, Italy, France, Thailand, Africa and America, Australia has limited research on new employee accident and incident rates available, reflecting a lack of focus on this issue. The Australian data shows that in general, the workforce is evolving and that the incident rates change depending on new employee rates.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".