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Record W3009122258 · doi:10.3310/hsdr08120

Factors influencing utilisation of ‘free-standing’ and ‘alongside’ midwifery units for low-risk births in England: a mixed-methods study

2020· article· en· W3009122258 on OpenAlexaboutno aff
Denis Walsh, Helen Spiby, Christine McCourt, Dawn Coleby, Celia P. Grigg, Simon Bishop, Miranda Scanlon, Lorraine Culley, Jane Wilkinson, Lynne Pacanowski, Jim Thornton

Bibliographic record

VenueHealth Services and Delivery Research · 2020
Typearticle
Languageen
FieldMedicine
TopicMaternal and Perinatal Health Interventions
Canadian institutionsnot available
FundersUniversity of OxfordHealth Services and Delivery Research ProgrammeNational Institute for Health and Care Research
KeywordsPsychological interventionQuarter (Canadian coin)ExcellenceStakeholderAuditClosure (psychology)MedicineNursingFamily medicineGeographyBusinessPublic relationsPolitical scienceAccounting

Abstract

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Background Midwifery-led units (MUs) are recommended for ‘low-risk’ births by the National Institute for Health and Care Excellence but according to the National Audit Office were not available in one-quarter of trusts in England in 2013 and, when available, were used by only a minority of the low-risk women for whom they should be suitable. This study explores why. Objectives To map the provision of MUs in England and explore barriers to and facilitators of their development and use; and to ascertain stakeholder views of interventions to address these barriers and facilitators. Design Mixed methods – first, MU access and utilisation across England was mapped; second, local media coverage of the closure of free-standing midwifery units (FMUs) were analysed; third, case studies were undertaken in six sites to explore the barriers and facilitators that have an impact on the development of MUs; and, fourth, by convening a stakeholder workshop, interventions to address the barriers and facilitators were discussed. Setting English NHS maternity services. Participants All trusts with maternity services. Interventions Establishing MUs. Main outcome measures Numbers and types of MUs and utilisation of MUs. Results Births in MUs across England have nearly tripled since 2011, to 15% of all births. However, this increase has occurred almost exclusively in alongside units, numbers of which have doubled. Births in FMUs have stayed the same and these units are more susceptible to closure. One-quarter of trusts in England have no MUs; in those that do, nearly all MUs are underutilised. The study findings indicate that most trust managers, senior midwifery managers and obstetricians do not regard their MU provision as being as important as their obstetric-led unit provision and therefore it does not get embedded as an equal and parallel component in the trust’s overall maternity package of care. The analysis illuminates how provision and utilisation are influenced by a complex range of factors, including the medicalisation of childbirth, financial constraints and institutional norms protecting the status quo. Limitations When undertaking the case studies, we were unable to achieve representativeness across social class in the women’s focus groups and struggled to recruit finance directors for individual interviews. This may affect the transferability of our findings. Conclusions Although there has been an increase in the numbers and utilisation of MUs since 2011, significant obstacles remain to MUs reaching their full potential, especially FMUs. This includes the capacity and willingness of providers to address women’s information needs. If these remain unaddressed at commissioner and provider level, childbearing women’s access to MUs will continue to be restricted. Future work Work is needed on optimum approaches to improve decision-makers’ understanding and use of clinical and economic evidence in service design. Increasing women’s access to information about MUs requires further studies of professionals’ understanding and communication of evidence. The role of FMUs in the context of rural populations needs further evaluation to take into account user and community impact. Funding This project was funded by the National Institute for Health Research (NIHR) Health Services and Delivery Research programme and will be published in full inHealth Services and Delivery Research; Vol. 8, No. 12. See the NIHR Journals Library website for further project information.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.014
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.189
GPT teacher head0.471
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations7
Published2020
Admission routes1
Has abstractyes

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