• Stephen John Walters, Cindy Stern, and Suzanne Robertson-Malt.
• 1The Joanna Briggs Institute, Faculty of Health Sciences, University of Adelaide, Australia 2University of Adelaide, Australia.
• JBI Database System Rev Implement Rep. 2016 Apr 1; 14 (4): 138-97.

BackgroundThere is a growing call by consumers and governments for healthcare to adopt systems and approaches to care to improve patient safety. Collaboration within healthcare settings is an important factor for improving systems of care. By using validated measurement instruments a standardized approach to assessing collaboration is possible, otherwise it is only an assumption that collaboration is occurring in any healthcare setting.ObjectivesThe objective of this review was to evaluate and compare measurement properties of instruments that measure collaboration within healthcare settings, specifically those which have been psychometrically tested and validated.Inclusion Criteria, Types Of ParticipantsParticipants could be healthcare professionals, the patient or any non-professional who contributes to a patient's care, for example, family members, chaplains or orderlies. The term participant type means the designation of any one participant; for example 'nurse', 'social worker' or 'administrator'. More than two participant types was mandatory.Types Of Intervention(S)/Phenomena Of InterestThe focus of this review was the validity of tools used to measure collaboration within healthcare settings.Types Of StudiesThe types of studies considered for inclusion were validation studies, but quantitative study designs such as randomized controlled trials, controlled trials and case studies were also eligible for inclusion. Studies that focused on Interprofessional Education, were published as an abstract only, contained patient self-reporting only or were not about care delivery were excluded.OutcomesThe outcome of interest was validation and interpretability of the instrument being assessed and included content validity, construct validity and reliability. Interpretability is characterized by statistics such as mean and standard deviation which can be translated to a qualitative meaning.Search StrategyThe search strategy aimed to find both published and unpublished studies. A three-step search strategy was utilized in this review. The databases searched included PubMed, CINAHL, Embase, Cochrane Central Register of Controlled Trials, Emerald Fulltext, MD Consult Australia, PsycARTICLES, Psychology and Behavioural Sciences Collection, PsycINFO, Informit Health Databases, Scopus, UpToDate and Web of Science. The search for unpublished studies included EThOS (Electronic Thesis Online Service), Index to Theses and ProQuest- Dissertations and Theses.Methodological QualityThe assessment of methodological quality of the included studies was undertaken using the COSMIN checklist which is a validated tool that assesses the process of design and validation of healthcare measurement instruments.Data CollectionAn Excel spreadsheet version of COSMIN was developed for data collection which included a worksheet for extracting participant characteristics and interpretability data.Data SynthesisStatistical pooling of data was not possible for this review. Therefore, the findings are presented in a narrative form including tables and figures to aid in data presentation. To make a synthesis of the assessments of methodological quality of the different studies, each instrument was rated by accounting for the number of studies performed with an instrument, the appraisal of methodological quality and the consistency of results between studies.ResultsTwenty-one studies of 12 instruments were included in the review. The studies were diverse in their theoretical underpinnings, target population/setting and measurement objectives. Measurement objectives included: investigating beliefs, behaviors, attitudes, perceptions and relationships associated with collaboration; measuring collaboration between different levels of care or within a multi-rater/target group; assessing collaboration across teams; or assessing internal participation of both teams and patients.Studies produced validity or interpretability data but none of the studies assessed all validity and reliability properties. However, most of the included studies produced a factor structure or referred to prior factor analysis. A narrative synthesis of the individual study factor structures was generated consisting of nine headings: organizational settings, support structures, purpose and goals; communication; reflection on process; cooperation; coordination; role interdependence and partnership; relationships; newly created professional activities; and professional flexibility.ConclusionsAmong the many instruments that measure collaboration within healthcare settings, the quality of each instrument varies; instruments are designed for specific populations and purposes, and are validated in various settings. Selecting an instrument requires careful consideration of the qualities of each. Therefore, referring to systematic reviews of measurement properties of instruments may be helpful to clinicians or researchers in instrument selection.Implications For PracticeSystematic reviews of measurement properties of instruments are valuable in aiding in instrument selection. This systematic review may be useful in instrument selection for the measurement of collaboration within healthcare settings with a complex mix of participant types. Evaluating collaboration provides important information on the strengths and limitations of different healthcare settings and the opportunities for continuous improvement via any remedial actions initiated.Implications For ResearchDevelopment of a tool that can be used to measure collaboration within teams of healthcare professionals and non-professionals is important for practice. The use of different statistical modelling techniques, such as Item Response Theory modelling and the translation of models into Computer Adaptive Tests, may prove useful. Measurement equivalence is an important consideration for future instrument development and validation. Further development of the COSMIN tool should include appraisal for measurement equivalence. Researchers developing and validating measurement tools should consider multi-method research designs.

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