Evidence map›Paper›PMID 28938878›Full record

ArticleBMC pregnancy and childbirth2017

Variation in clinical decision-making for induction of labour: a qualitative study.

Tanya A Nippita, Maree Porter, Sean K Seeho, Jonathan M Morris, Christine L Roberts

Open access · goldAbstract read
In one paragraph

Article in BMC pregnancy and childbirth, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.9field-weighted citation impact, top 14% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 36 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 3 institutions in 1 country.

Tanya A NippitaClinical and Population Perinatal Health Research, Kolling Institute of Medical Research, Northern Sydney Local Health District, Level 5, Douglas Building, Royal North Shore Hospital, St Leonards, NSW, 2065, Australia. tanya.nippita@sydney.edu.au.
Maree PorterClinical and Population Perinatal Health Research, Kolling Institute of Medical Research, Northern Sydney Local Health District, Level 5, Douglas Building, Royal North Shore Hospital, St Leonards, NSW, 2065, Australia.
Sean K SeehoClinical and Population Perinatal Health Research, Kolling Institute of Medical Research, Northern Sydney Local Health District, Level 5, Douglas Building, Royal North Shore Hospital, St Leonards, NSW, 2065, Australia.
Jonathan M MorrisClinical and Population Perinatal Health Research, Kolling Institute of Medical Research, Northern Sydney Local Health District, Level 5, Douglas Building, Royal North Shore Hospital, St Leonards, NSW, 2065, Australia.
Christine L RobertsClinical and Population Perinatal Health Research, Kolling Institute of Medical Research, Northern Sydney Local Health District, Level 5, Douglas Building, Royal North Shore Hospital, St Leonards, NSW, 2065, Australia.
Northern Sydney Local Health District · AURoyal North Shore Hospital · AUUniversity of Sydney · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundUnexplained variation in induction of labour (IOL) rates exist between hospitals, even after accounting for casemix and hospital differences. We aimed to explore factors that influence clinical decision-making for IOL that may be contributing to the variation in IOL rates between hospitals.

methodsWe undertook a qualitative study involving semi-structured, audio-recorded interviews with obstetricians and midwives. Using purposive sampling, participants known to have diverse opinions on IOL were selected from ten Australian maternity hospitals (based on differences in hospital IOL rate, size, location and case-mix complexities). Transcripts were indexed, coded, and analysed using the Framework Approach to identify main themes and subthemes.

resultsForty-five participants were interviewed (21 midwives, 24 obstetric medical staff). Variations in decision-making for IOL were based on the obstetrician's perception of medical risk in the pregnancy (influenced by the obstetrician's personality and knowledge), their care relationship with the woman, how they involved the woman in decision-making, and resource availability. The role of a 'gatekeeper' in the procedural aspects of arranging an IOL also influenced decision-making. There was wide variation in the clinical decision-making practices of obstetricians and less accountability for decision-making in hospitals with a high IOL rate, with the converse occurring in hospitals with low IOL rates.

conclusionImproved communication, standardised risk assessment and accountability for IOL offer potential for reducing variation in hospital IOL rates.

Indexed as

Clinical Decision-MakingLabor, InducedMidwiferyObstetricsAustraliaFemaleHealth Services AccessibilityHumansInterviews as TopicPatient ParticipationPhysician-Patient RelationsPractice Guidelines as TopicPractice Patterns, Physicians'PregnancyQualitative ResearchReferral and ConsultationDecision-makingDoctor-patient relationshipLabour inductionObstetricsRisk assessmentVariation

Identifiers

PMID28938878
PMCPMC5610463
OpenAlexW2760572860

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.