Bibliographic record
Abstract
The effectiveness of occupational health and safety management systems (OHSMS) can be understood through analysis of surveys such as the experiences of exposure to occupational hazards by Australian nursing occupations. How effectively OHSMS are implemented in the Australian health industry is unclear as few studies describe current hazard exposure patterns or the impact of OHSMS in the Australian health industry. This paper concludes from the analysis of an Exposure Survey of Australian nursing occupations that nursing occupations perceive themselves to be "at risk" of injury and/or management of OHS risk in work duties is affected by the patterns of hazard exposure, occupation group as well as employee attributes, perceptions, patterns and situations of work. The results highlight the top-rated hazards and imply that the perceptions of hazards in the workplace are different to actual risk experience (e.g. injury patterns). There is an unacceptable level of exposure to diverse hazards in Australian nursing occupations workplaces in regard to regulatory and performance obligations. Stronger strategies to achieve more effective risk treatment, integrate with hospital accreditation and quality programs are discussed to benefit system performance and the welfare of those in nursing occupations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".