The development and testing of an algorithm for diagnosis of active labour in primiparous women

Citation

Cheyne H, Dowding D, Hundley V, Aucott L, Styles M, Mollison J, Greer I & Niven C (2008) The development and testing of an algorithm for diagnosis of active labour in primiparous women. Midwifery, 24 (2), pp. 199-213. https://doi.org/10.1016/j.midw.2006.12.005

Abstract
Objectives: to describe the development and testing of an algorithm for diagnosis of active labour in primiparous women. Design: qualitative and quantitative methods were used. A literature review was first conducted to identify the key cues for inclusion in the algorithm. Focus groups of midwives were then conducted to assess content validity, finally a vignette study assessed the inter-rater reliability of the algorithm. Setting: midwives from two study sites were invited to participate. Data were collected during 2002 and 2003. Participants: midwives from the first site took part in the focus groups (n=13), completed vignettes (n=19), or both. Midwives from the second site then completed vignettes (n=17). Findings: an algorithm, developed from the key informational cues reported in the literature, was validated in relation to content validity by the findings from the focus groups. Inter-rater reliability was tested using vignettes of admission case histories and was found to be moderate in the first test (K=0.45). However, after modifying the algorithm the kappa score was 0.86, indicating a high level of agreement. Key conclusions: diagnosis of labour may be straightforward on paper but is frequently problematic in practice. This may be because the diagnosis of labour is made in a high pressured environment where conflicting pressures of workload, limited resources and emotional pressures add to the complexity of the judgement. Implications for practice: we offer a valid and reliable decision-support tool as an aid for diagnosis of labour. The evaluation of the implementation of this tool is under way and will determine whether it is effective in reducing unnecessary admissions and improving clinical outcomes for women.

Keywords
Labour; Diagnosis; Algorithm; Judgement; Decision-making; Labor (Obstetrics); Childbirth; Midwifery

Journal
Midwifery: Volume 24, Issue 2