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Data · dataset · 2026

Implementation of a multi-component strategy to optimise the timing and use of induction of labour: a multiple-methods evaluation

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Description

Background

Induction of labour rates are rising worldwide and, in certain contexts, are associated with poorer outcomes for some women and babies. In Australia, the induction rate has increased by 8% to 33% since 2010. At a large regional health service in Victoria, Australia, maternity staff developed a toolkit (guidance document and forms) to improve induction of labour decision-making and processes.

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Aims To evaluate the impact of implementing the induction of labour toolkit to reduce variation in practice and support IOL decision making processes in a single site, regional health service.

Methods

Multiple-method, descriptive study design. Phase 1 - audit of routinely collected perinatal data, 11 months pre- and 11 months post-toolkit implementation; Phase 2 - focus group interview with clinicians. Findings 1,849 women (970 pre-implementation and 879 post-implementation) underwent induction of labour, with no statistically significant difference between groups (970/2571 versus 879/2440, p=0.212).

Toolkit implementation was not associated with significant differences in labour, birth, or neonatal outcomes between groups. Four themes were identified through thematic analysis of the interview data: three related to improved induction of labour practice: 1) consistent information sharing with women and families, 2) embedding best practice, and 3) improved interprofessional collaboration. The final theme reflected the barriers to toolkit adherence: 4) challenges operationalising the process.

Conclusions Qualitative findings suggest that implementing an induction of labour toolkit that includes evidence-based guidance can support clinicians’ practice. Health services need to integrate strategies to support effective implementation of processes and practices, with further evaluation of the implementation and sustainability.

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Topics

Provenance · 1 source records, 12 field assertions
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ZivaHuboai:figshare.com:article/3402114013 d agoJSON v1
FieldAssertionExtractorEvidence
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