Relationship Between Opioid Use and Pain Severity Ratings in Workers With Low Back Pain

Nursing Faculty Professional Quality of Life and Resilience in a Changing Educational Environment

BACKGROUND

Growing workload demands of nursing faculty has led to an academic work environment that is stressful with loss of work life balance and occupational burnout.

METHOD

Full-time faculty were surveyed prior to the pandemic at a private school of nursing using the Professional Quality of Life (ProQOL), Bride Secondary Traumatic Stress Scale (STSS), Conner-Davidson Resilience-10 (CDRS-10), and Trauma Informed Climate Scale (TICS-10).

RESULTS

The ProQOL revealed moderate faculty burnout scores (mean score, 25.8). The STSS identified two thirds of faculty experienced secondary traumatic stress in various degrees, with one third of faculty respondents experiencing high to severe STSS symptoms. The CDRS-10 identified 42% of faculty scored within the lowest quartile and 13% of faculty scored within the highest quartile.

CONCLUSION

Faculty must be provided a safe work environment to limit burn-out, promote resilience, and support work life balance to address the current nursing faculty shortage. [J Nurs Educ. 2022;61(6):330-333.].

Incidence of Workers' Compensation Claims in Opioid-Using Truck Drivers

OBJECTIVE

This study examines the relationship between opioid use prevalence and subsequent filing of workers' compensation claims.

METHODS

A retrospective cohort study design was utilized to examine data from drivers' initial commercial driver medical exam, employment data, and workers' compensation claims data.

RESULTS

Data from 57,733 over 7 years were analyzed. Drivers who reported opioid use at their initial medical exam visit filed subsequent workers' compensation claims 1.81 times sooner (P = 0.0001; 95% CI 1.34, 2.44) than drivers who did not report opioid use at their CDME when controlling for age, gender, BMI, and diastolic blood pressure.

CONCLUSIONS

These findings provide information that may aid in improving regulations to control for incidents, training programs to inform professional drivers of factors that increase accident risk and educating prescribers about increased risks of injury among opioid-using drivers.

A forecasting framework for predicting perceived fatigue: Using time series methods to forecast ratings of perceived exertion with features from wearable sensors

Advancements in sensing and network technologies have increased the amount of data being collected to monitor the worker conditions. In this study, we consider the use of time series methods to forecast physical fatigue using subjective ratings of perceived exertion (RPE) and gait data from wearable sensors captured during a simulated in-lab manual material handling task (Lab Study 1) and a fatiguing squatting with intermittent walking cycle (Lab Study 2). To determine whether time series models can accurately forecast individual response and for how many time periods ahead, five models were compared: naïve method, autoregression (AR), autoregressive integrated moving average (ARIMA), vector autoregression (VAR), and the vector error correction model (VECM). For forecasts of three or more time periods ahead, the VECM model that incorporates historical RPE and wearable sensor data outperformed the other models with median mean absolute error (MAE) <1.24 and median MAE <1.22 across all participants for Lab Study 1 and Lab Study 2, respectively. These results suggest that wearable sensor data can support forecasting a worker's condition and the forecasts obtained are as good as current state-of-the-art models using multiple sensors for current time prediction.