The timing and type of nursing staff occupational injury and illness incidents, Veterans Health Administration, 2002-2011: a retrospective, population-based, descriptive analysis
Bibliographic record
Abstract
Background: While the majority of occupational injuries and illnesses that result in lost work days occur during typical day shift hours, the U.S. Bureau of Labor Statistics has noted that timing patterns often reflect the unique nature of different occupations. However, a literature search indicated that few studies have assessed the interplay between the timing and the type of nursing staff occupational injury and illness incidents in general, but especially so for those incidents that were recorded on an hourly basis. Methods : This decade-long retrospective population-based study ascertained the timing of diverse types of reported occupational injury or illness incidents among Veterans Health Administration (VHA) nursing employees, who were classified between the nurse, practical nurse, and nursing assistant series. Using January 1, 2002 as the start date for the longitudinal surveillance of incidents, descriptive analyses included 55,424 VHA nursing employees who reported a total of 113,708 incidents between 2002 and 2011, of which 106,216 (93.4%) were retained for this study, because they included both the specific time and the specific type of incident involved. Although nursing staff work shifts can vary widely, three “typical” 8-hour work shifts–that is to say, night shift: 23:01-07:00, day shift: 07:01-15:00, and evening shift: 15:01-23:00–were selected for summarizing study findings (i.e., for incidents that included both the specific time and the specific type of incident involved). Results: Findings indicated that male nursing staff (accounting for 15.4% of the applicable occupational injury and illness incidents) reported a larger percentage of “Assaults” and “Lifting (Patient Care)” incidents, especially during the evening and the night shifts, whereas female nursing staff (accounting for 84.6% of the applicable incidents) reported a larger percentage of “Slips, Trips, and Falls” incidents, but these were more likely to occur during the beginning and the end of each shift. Findings also indicated that, regardless of gender, “Assaults” and “Lifting (Patient Care)” incidents were more commonly reported during the evening and the night shifts, as compared with the day shift, between all three nursing occupations (nurse, practical nurse, and nursing assistant). “Slips, Trips, and Falls” incidents were more commonly reported during the beginning and end of each shift, between all three nursing occupations. Conclusions: Staffing patterns and nursing staff working conditions are risk factors for occupational injuries and illnesses. Findings suggest that more attention is needed for ascertaining the potential role and functioning of targeted injury prevention training initiatives with respect to the timing and potential likelihood of selected types of nursing staff occupational injury or illness incidents.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".