
doi: 10.1111/jan.12467
pmid: 24961297
AbstractAimTo map current selection and recruitment processes for newly qualified nurses and to explore the advantages and limitations of current selection and recruitment processes.BackgroundThe need to improve current selection and recruitment practices for newly qualified nurses is highlighted in health policy internationally.DesignA cross‐sectional, sequential‐explanatory mixed‐method design with 4 components: (1) Literature review of selection and recruitment of newly qualified nurses; and (2) Literature review of a public sector professions’ selection and recruitment processes; (3) Survey mapping existing selection and recruitment processes for newly qualified nurses; and (4) Qualitative study about recruiters’ selection and recruitment processes.MethodsLiterature searches on the selection and recruitment of newly qualified candidates in teaching and nursing (2005–2013) were conducted. Cross‐sectional, mixed‐method data were collected from thirty‐one (n = 31) individuals in health providers in London who had responsibility for the selection and recruitment of newly qualified nurses using a survey instrument. Of these providers who took part, six (n = 6) purposively selected to be interviewed qualitatively.ResultsIssues of supply and demand in the workforce, rather than selection and recruitment tools, predominated in the literature reviews. Examples of tools to measure values, attitudes and skills were found in the nursing literature. The mapping exercise found that providers used many selection and recruitment tools, some providers combined tools to streamline process and assure quality of candidates.ConclusionsMost providers had processes which addressed the issue of quality in the selection and recruitment of newly qualified nurses. The ‘assessment centre model’, which providers were adopting, allowed for multiple levels of assessment and streamlined recruitment. There is a need to validate the efficacy of the selection tools.
Data Collection, Humans, Nurses, Personnel Selection, RT
Data Collection, Humans, Nurses, Personnel Selection, RT
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