Bibliographic record
Abstract
Machine related hazardous situations are still resulting in many serious accidents in industries. For example, around 13000 accidents involving machines take place every year in the province of Quebec, Canada. Even if safety of machinery is a major concern in the manufacturing sector, machines are also present in many other fields of activities, including healthcare. Indeed, machine related risks and accidents are also present in hospitals. In this particular environment, machines such as food preparation equipment, laundry equipment, HVAC equipment, lifts and elevators and maintenance related machinery and tools are commonly used. With the importance of machine related accidents, the risk management practices for safety of machinery in the manufacturing sector are well known and documented. However, there is very little knowledge about the importance of machinery related risks and their management practices in the case of hospitals. The exploratory study presented this paper proposes to address (i) the detailed statistics of machinery related accidents in hospitals; (ii) the characteristics of the machines used and their inherent hazards; and (iii) the level of integration of risk management practices for safety of machinery in hospitals, such as risk assessment, machine safeguarding, safe work procedures, training and personal protective equipment.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".