Bibliographic record
Abstract
In their survey of Iowa and Virginia hospitals, Beekmann et al. report estimates of percutaneous injury rates for nursing personnel relative to two prior multihospital and several single-hospital studies, and comment that these injuries remain common even after promulgation of the Occupational Safety and Health Administration's Bloodborne Pathogens Standard. 1 It is difficult to compare injury rates unless they incorporate corrections for underreporting and, especially in overtimeprone understaffed units, number of hours worked (thus, at risk).A decade ago, a study of 312 critical care nurses in 11 self-selected, acutecare Canadian hospitals found injury attack and incidence density rates commensurate with rates published prior to the era of Universal Precautions and Body Substance Isolation, no significant reduction in rates following adoption of Universal Precautions and Body Substance Isolation, no correlation between reduction of needlestick injury and extent of recapping (estimated by inspection of disposal containers), and significant underreporting of employee injuries. 2-3 At that time, the strategy perceived as least effective in discouraging recapping also was the most prevalent. 4 These 11 hospitals were a subset of the large number of hospitals participating in a survey of infection control program practices. 5 Overall, we found the staffing levels of infection control programs to be consistent with the finding of Beekmann et al. that the smallest hospitals were least likely to have infection control staff, but also found low staffing ratios of infection control professionals in larger hospitals (Table ).
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 | 0.003 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.016 | 0.015 |
| Insufficient payload (model declined to judge) | 0.026 | 0.022 |
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".