Pre-hospital opioid analgesia for traumatic injuries

Factors influencing nurses' pain assessment and management of road traffic casualties: a qualitative study at a military hospital in Ghana

BACKGROUND

Evidence shows that patients who visit the surgical and trauma emergency units may be discharged with untreated or increased pain levels. This study explored nurses' pain assessment and management approaches at a trauma-surgical emergency unit in Ghana.

METHODS

Seventeen nurses who work in the trauma department participated in this qualitative exploratory descriptive study. In-depth individual interviews were conducted, and the thematic analysis was utilized to identify emerging themes and subthemes.

RESULTS

Three main themes were identified: patient pain indicators, pain management, and institutional factors influencing pain management. The study revealed that nurses rely on verbal expressions, non-verbal cues, physiological changes, and the severity of pain communicated. The findings highlighted staff shortage, inadequate resources, and lack of standardized guidelines as factors affecting pain and management.

CONCLUSIONS

Although the study offers critical new perspectives on nurses' experiences regarding pain related issues at the trauma-surgical emergency units, its small sample size limited its generalizability.

Identifying Risk Factors for Drug Use in an Iranian Treatment Sample: A Prediction Approach Using Decision Trees

INTRODUCTION AND AIM

Substance abuse exacts considerable social and health care burdens throughout the world. The aim of this study was to create a prediction model to better identify risk factors for drug use.

DESIGN AND METHODS

A prospective cross-sectional study was conducted in South Khorasan Province, Iran. Of the total of 678 eligible subjects, 70% (n: 474) were randomly selected to provide a training set for constructing decision tree and multiple logistic regression (MLR) models. The remaining 30% (n: 204) were employed in a holdout sample to test the performance of the decision tree and MLR models. Predictive performance of different models was analyzed by the receiver operating characteristic (ROC) curve using the testing set. Independent variables were selected from demographic characteristics and history of drug use.

RESULTS

For the decision tree model, the sensitivity and specificity for identifying people at risk for drug abuse were 66% and 75%, respectively, while the MLR model was somewhat less effective at 60% and 73%. Key independent variables in the analyses included first substance experience, age at first drug use, age, place of residence, history of cigarette use, and occupational and marital status.

DISCUSSION AND CONCLUSION

While study findings are exploratory and lack generalizability they do suggest that the decision tree model holds promise as an effective classification approach for identifying risk factors for drug use. Convergent with prior research in Western contexts is that age of drug use initiation was a critical factor predicting a substance use disorder.