Modeling Missed Care: Implications for Evidence‐Based Practice. Issue 3 (23rd March 2018)

• Record Type: Journal Article
• Authors: Blackman, Ian; Lye, Che Yee; Darmawan, I Gusti Ngurah; Henderson, Julie; Giles, Tracey; Willis, Eileen; Toffoli, Luisa; Xiao, Lily; Verrall, Claire
• Is Part Of: Worldviews on evidence-based nursing. Volume 15:Issue 3(2018)
• Page Start: 178
• Page End: 188
• Publication Date: 2018-03-23
• Subjects: missed care -- predictor variables -- shift times -- skill mix -- staff patient ratios; Evidence-based nursing -- Periodicals; 610.7
• DOI: 10.1111/wvn.12285
• Languages: English
• ISSNs: 1545-102X

Abstract

ABSTRACT: Background: There is a growing nursing literature that views missed care as an inevitable consequence of work intensification associated with the rationing of nursing and material resources available to deliver care. Global studies recognize that missed care is now ubiquitous, although studies tend to be conducted in one region, rather than nationwide. This study seeks to understand the Australian context of missed care. Aims: To explore self‐reported reasons for missed care and to identify the main factors for predicting missed care within a sample of Australian nurses and midwives working in public and private hospitals in New South Wales, Victoria, Tasmania, and South Australia. Methods: A nonexperimental, descriptive method using Kalisch's (2006) MISSCARE survey was used. Responses from 1, 195 nursing and midwifery staff with differing qualifications, English language skills, and Australian employment settings were analyzed using Rasch analysis and then modeled using the Structural Equation Modeling. Results: The frequency of missed care on the morning shift directly impacted on higher priority care missed during the afternoon shift. Staff skill mix imbalances and perceived inadequacy of staff numbers for the work demands further exacerbated all aspects of care during afternoon shifts. Other major factors associated with missed care were the different clinical work settings and staff to patient ratios. Linking Evidence to Action: The incidences, types, and reasons behind missed care are a multidimensional construct which can be predicted when known significant factors behind missed care are simultaneously accounted for.