title: The development and testing of an algorithm for diagnosis of active labour in primiparous women.
abstract: OBJECTIVE
  to describe the development and testing of an algorithm for diagnosis of active labour in primiparous women.
  
  
  METHODS
  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.
  
  
  METHODS
  midwives from two study sites were invited to participate. Data were collected during 2002 and 2003.
  
  
  METHODS
  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).
  
  
  RESULTS
  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.
  
  
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: University of Stirling, Stirling, FK9 4LA, Scotland, UK. h.l.cheyne@stir.ac.uk
authors.forename: Helen
authors.initials: H
authors.lastname: Cheyne
authors.forename: Dawn
authors.initials: D
authors.lastname: Dowding
authors.forename: Vanora
authors.initials: V
authors.lastname: Hundley
authors.forename: Lorna
authors.initials: L
authors.lastname: Aucott
authors.forename: Maggie
authors.initials: M
authors.lastname: Styles
authors.forename: Jill
authors.initials: J
authors.lastname: Mollison
authors.forename: Ian
authors.initials: I
authors.lastname: Greer
authors.forename: Catherine
authors.initials: C
authors.lastname: Niven
mesh_terms: D000328:Adult; D000465:Algorithms; D005260:Female; D017144:Focus Groups; D006302:Health Services Research; D006801:Humans; D007231:Infant, Newborn; D007745:Labor Onset; D008880:Midwifery / Q000379:methods*; D024802:Nurse's Role; D009733:Nursing Diagnosis / Q000379:methods*; D015403:Nursing Evaluation Research; D009775:Obstetrics and Gynecology Department, Hospital / Q000458:organization & administration; D017063:Outcome Assessment, Health Care; D011247:Pregnancy; D011594:Psychometrics; D006113:United Kingdom