The effect of power stretchers on occupational injury rates in an urban emergency medical services system
Bibliographic record
Abstract
BACKGROUND: To examine occupational injury rates in a dual-response emergency medical services (EMS) system before and after implementation of a power-lift stretcher system. METHODS: The seasonally-adjusted occupational injury rate was estimated relative to medical call volume (per 1000 calls) and workers (per 100 FTEs) from 2009 to 2019, and stratified by severity (lost-time, healthcare only), role (EMS, FIRE) and type (patient-handling). Power-lift stretchers were adopted between 2013 and 2015. Preinjury versus postinjury rates were compared using binomial tests. Interrupted time series (ITS) analysis was used to estimate the trend and change in injuries related to patient-handling, with occupational illnesses serving as control. RESULTS: Binomial tests revealed varied results, with reductions in the injury rate per 1000 calls (-14.0%) and increases in the rate per 100 FTEs (+14.1%); rates also differed by EMS role and injury severity. ITS analysis demonstrated substantial reductions in patient-handling injuries following implementation of power-lift stretchers, both in the injury rate per 1000 calls (-50.4%) and per 100 FTEs (-46.6%), specifically among individuals deployed on the ambulance. Injury rates were slightly elevated during the winter months (+0.8 per 100 FTEs) and lower during spring (-0.5 per 100 FTEs). CONCLUSIONS: These results support the implementation of power-lift stretchers for injury prevention in EMS systems and demonstrate advantages of ITS analysis when data span long preintervention and postintervention periods.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".