Nurses' Knowledge and Treatment Beliefs: Use of Complementary and Alternative Medicine for Pain Management.
Nicole J BrewerStephanie L TurriseYeoun Soo Kim-GodwinRichard S PondPublished in: Journal of holistic nursing : official journal of the American Holistic Nurses' Association (2019)
Purpose: To examine the relationship between nurses' knowledge, attitudes, and beliefs about medicines, in general, and complementary and alternative medicine (CAM) and identify the predictors of referrals for pain management. Method: This descriptive, correlational study utilized an online survey to collect data from direct care nurses at a large medical center in southeastern United States. The online survey consisted of the Complementary and Alternative Medicines and Beliefs Inventory (CAMBI), the Beliefs about Medicine Questionnaire, and four open-ended questions. Referral data were obtained from the Information Management Department at this medical center. Results: Among the 218 nurses who completed the survey (15.12%), majority (85%) supported CAM use, but only 32% reported utilizing CAM therapies with patients. Medical surgical, emergency department, and perioperative nurses scored higher on their CAMBI total score and were more likely to refer for CAM therapies when compared with intensive care unit nurses. Conclusions: Beliefs about CAM specifically were not related to referrals for CAM therapies. This study suggests the need for further education on the nurse's role in CAM usage. Understanding the link between nurses' knowledge, attitudes, and treatment beliefs and their relationship to CAM usage provides direction for future educational interventions.
Keyphrases
- healthcare
- pain management
- mental health
- emergency department
- cross sectional
- intensive care unit
- chronic pain
- end stage renal disease
- chronic kidney disease
- health information
- electronic health record
- patients undergoing
- newly diagnosed
- machine learning
- ejection fraction
- peritoneal dialysis
- minimally invasive
- mechanical ventilation
- prognostic factors
- deep learning
- drug induced