Modeling Missed Care: Implications for Evidence-Based Practice

Ian Blackman, Che Lye, Gusti Darmawan, Julie Henderson, Tracey Giles, Eileen Willis, Luisa Toffoli, Lily Xiao, Claire Verrall

Research output: Contribution to journalArticlepeer-review

27 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)178-188
Number of pages11
JournalWorldviews on Evidence-Based Nursing
Issue number3
Early online date2018
Publication statusPublished - Jun 2018


  • missed care
  • predictor variables
  • shift times
  • skill mix
  • staff patient ratios


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