Bibliographic record
Abstract
Aims: The purpose paper aims to report a vulnerability of young workers regarding their health and safety.Design/methodology/approach: A systematic search was conducted through PubMed, Scopus, Web of Science, Science Direct and Google Scholar using terms of interest in a logic grid with key words “young workers” and “health and safety”. The articles in this search were limited to those published between 2002 and 2012. Nine studies ( 9 papers) met the inclusion criteria and were independently reviewed.Findings :Majority of the studies in this synopsis indicate education is a key to reduce young workers injury. In the other hands, the current safety education and training for young workers apparently are not effective due to the training uses similar method that is applied to adult workers, which is not suited to youths’ developmental levels or interest. Furthermore, the safety training alsoshould focus not only on providing information for identifying reasons for workplace injuries and young workers’ rights but should be also embedded into workplace safety education programs. The safety training should become a priority in health education programs applied in schools. Research limitations/implications: Due to the necessary Australia focus and time constraints, only studies from Canada, USA, New Zealand and Denmark were included. Originality/value: The paper shows that there is a evidence base that education and knowledge about hazards interventions can positively reduce young workers injury.
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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.023 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".