Decisions about nurse staffing models are a concern for health systems globally due to workforce retention and well-being challenges. Nurse staffing models range from all Registered Nurse workforce to a mix of differentially educated nurses and aides (regulated and unregulated), such as Licensed Practical or Vocational Nurses and Health Care Aides. Systematic reviews have examined relationships between specific nurse staffing models and client, staff and health system outcomes (eg, mortality, adverse events, retention, healthcare costs), with inconclusive or contradictory results. No evidence has been synthesised and consolidated on how, why and under what contexts certain staffing models produce different outcomes. We aim to describe how we will (1) conduct a realist review to determine how nurse staffing models produce different client, staff and health system outcomes, in which contexts and through what mechanisms and (2) coproduce recommendations with decision-makers to guide future research and implementation of nurse staffing models.
Using an integrated knowledge translation approach with researchers and decision-makers as partners, we are conducting a three-phase realist review. In this protocol, we report on the final two phases of this realist review. We will use Citation tracking, tracing Lead authors, identifying Unpublished materials, Google Scholar searching, Theory tracking, ancestry searching for Early examples, and follow-up of Related projects (CLUSTER) searching, specifically designed for realist searches as the review progresses. We will search empirical evidence to test identified programme theories and engage stakeholders to contextualise findings, finalise programme theories document our search processes as per established realist review methods.
Ethical approval for this study was provided by the Health Research Ethics Board of the University of Alberta (Study ID Pro00100425). We will disseminate the findings through peer-reviewed publications, national and international conference presentations, regional briefing sessions, webinars and lay summary.
To explore how emerging adult-aged women self-manage their sexual and reproductive health and to generate a grounded theory of these self-management processes.
Grounded theory methods using a constructivist approach.
Between September 2019 and September 2020, 18‑ to 25-years-old women (n = 13) were recruited from a 4-year university, a 2-year community college, and neighbourhoods surrounding the institutions of higher education. Individual interviews were transcribed verbatim and qualitatively analysed using a constant comparative method and inductive coding.
The theory purports that core processes of sexual and reproductive health self-management used by the women in this study included both passive and (re)active processes. These processes expanded upon and/or maintained the women's accessible sexual and reproductive health knowledge, behaviour and beliefs, defined as the sexual and reproductive health repertoire. The processes appeared to be cyclical and were often initiated by a catalysing event or catalyst and resulted in conversations with confidantes, or trusted individuals. A catalyst was either resolved or normalized by expanding or maintaining the sexual and reproductive health repertoire.
The resulting theory, EMeRGE Theory, offers insight into the complex and cyclical processes emerging adult-aged women use to simultaneously develop and adapt their foundational sexual and reproductive health knowledge, behaviours and beliefs.
This explication of emerging adult-aged women's sexual and reproductive health self-management processes can be used by nurses and nurse researchers to better address this population's unique health needs.
The EMeRGE Theory provides valuable guidance for future exploratory and intervention research aimed at improving the health and well-being of emerging adult-aged women.
The authors adhered to the Consolidated Criteria for Reporting Qualitative studies (COREQ) in preparation of this publication.
No patient or public contribution.